Method for balancing frequency instability on an electric grid using networked distributed energy storage systems

ABSTRACT

Embodiments of the present invention include control methods employed in multiphase distributed energy storage systems that are located behind utility meters typically located at, but not limited to, medium and large commercial and industrial locations. These distributed energy storage systems can operate semi-autonomously, and can be configured to develop energy control solutions for an electric load location based on various data inputs and communicate these energy control solutions to the distributed energy storage systems. In some embodiments, one or more distributed energy storage systems may be used to absorb and/or deliver power to the electric grid in an effort to provide assistance to or correct for power transmission and distribution problems found on the electric grid outside of an electric load location. In some cases, two or more distributed energy storage systems are used to form a controlled and coordinated response to the problems seen on the electric grid.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application titled,“METHOD FOR BALANCING FREQUENCY INSTABILITY ON AN ELECTRIC GRID USINGNETWORKED DISTRIBUTED ENERGY STORAGE SYSTEMS,” filed Sep. 13, 2013 andhaving Ser. No. 14/026,993, which claims the benefit of United StatesProvisional patent application titled “CONTROL METHOD FOR DELIVERINGPOWER USING EXTERNAL DATA”, filed Sep. 13, 2012 and having Ser. No.61/700,840, and also claims the benefit of United States Provisionalpatent application titled “METHOD FOR BALANCING FREQUENCY INSTABILITY ONAN ELECTRIC GRID USING NETWORKED DISTRIBUTED ENERGY STORAGE SYSTEMS”,filed Mar. 15, 2013 and having Ser. No. 61/801,274. The subject matterof these related applications is hereby incorporated herein byreference.

BACKGROUND OF THE INVENTION

Field of the Invention

Embodiments of the present invention generally relate to a method and anapparatus for controlling fluctuations in power and amount of power usedat an electric load location and/or on an electrical grid.

Description of the Related Art

Energy demand at a commercial site, such as a business, or at home willvary over the time of day. In a typical home or hotel setting, there isa peak in the morning when the occupants get up and when the occupantsreturn home at the end of the day. This typically creates two demandpeaks during a normal day. Commercial buildings tend to follow differentpatterns depending on the nature of the business. For example, usage istypically low when a commercial building is closed, and may berelatively constant or fluctuate between moderate to high levelsdepending on the type of business when the building is open. Forexample, a car wash may have more fluctuations in its energy use than anoffice building in a moderate climate.

The cost to a utility for generating or purchasing electrical energyincreases dramatically during periods of peak use versus periods ofoff-peak usage. In order to compensate for the higher peak-hours costs,utility companies often employ time of day-based rate schedules,charging a significantly higher rate for electrical energy (e.g., costper kilowatt-hour (kW-hr)) consumed during peak usage hours as comparedto energy consumed during off-peak hours. For example, homes andbusinesses may pay for electricity on a per-kilowatt hour basis with onerate applying during off-peak hours, and another, higher, rate applyingduring peak hours. The higher rates charged during peak usage periodscan lead to significantly higher energy costs for the user, especiallywhen the user's period(s) of high demand coincides with or falls withinthe interval set by the utility as peak hours.

Devices have been developed that help users reduce the cost ofelectricity purchases from the power grid by storing electricity inenergy storage mediums, such as batteries, that can be “drawn down”during peak hours to reduce demand from the grid. The batteries can becharged during non-peak hours, thus reducing the total cost ofelectricity, and, during favorable conditions, electricity can even besold back to the grid. This process is often referred to as “energyarbitrage,” which is generally the storing of energy at one time of dayand then the discharging of energy at another time, effectively shiftingenergy consumption from one time period to another.

Energy storage mediums, especially battery energy storage, are expensiveand, while various techniques are known by which an energy storagesystem can be used to optimize energy use at a business or home, suchtechniques are generally inefficient in applying stored energy toeffectively control the energy use at an electric load location.Consequently, an impractical quantity of energy storage mediums arerequired at an electric load location to realize useful energyarbitrage. Generally, such energy storage systems use simple methods forcontrolling the charging and discharging of power provided to offsetpeak demands. For example, two approaches commonly used in energystorage systems include: Using a simple timer to control charge times ofthe energy storage system (typically during off-peak hours) anddischarge times (typically during peak demand hours); and using a singledemand set-point that the storage system reacts to while monitoring andcontrolling the energy use of the business or home location. A singledemand set-point generally is a single level set-point to which thecontrolling element in an energy storage system will control duringoperation. Each of these approaches generally requires an uneconomicalamount of energy storage capacity in order to offset demand peaks at theelectric load location. Furthermore, use of a single demand set-pointtypically results in an energy storage system running out of energystorage availability due to over-reaction of the controlling componentsto the demand set point set by the controlling system. Thus, a needexists for power charge and discharge systems and methods that moreeffectively utilize the consumable energy storage medium components inan energy storage system.

Today's electrical power grid in the United States consists primarily oflarge synchronous power generators, such as hydroelectric generatingfacilities and natural gas combustion turbines. In the United States,these types of power generators generate electricity to meet demand at afrequency of 60 Hz. If a source of electricity from a large powergenerator (e.g., power plant) is dramatically reduced, system frequencyand the speed of other system interconnected generators, which areelectrically connected to the grid, will decrease. To compensate for theshift in frequency, the other conventional power generators, at one ormore generating facilities that are attached to the grid, automaticallyrespond via their governor control schemes by creating more power toincrease the system generators' speed and frequency, attempting to bringsystem frequency back to 60 Hz. Additionally, in an oversupplysituation, for example if a wind farm unexpectedly increases productionthrough a large wind deviation over its scheduled production level,system frequency increases and the other synchronous generators that areconnected to the grid reduce their production to automatically bring thesystem frequency back down closer to 60 Hz. The stability of theelectrical power grid has been changing with an increase in the numberof wind and solar generators that are not equipped with automaticgovernor controls and are displacing the quantity of power delivered bytraditional synchronous generators. Not only is wind and solargeneration unpredictable, but these generators also have no automaticcontrol schemes to help maintain system synchronous speed and no eventresponse capability during their process of delivering power to thegrid. This creates a significant and growing need for automaticfrequency balancing grid-connected devices to maintain 60 Hz. Thesedevices must not only be able to balance frequency events frequently andrepeatedly, but also act very quickly to help stabilize the systemfrequency as the radiation of the sun and the speed of the wind changerenewable production levels constantly and near instantaneously.Therefore, there is a need for a method and apparatus that is able tohelp minimize, reduce and/or prevent and correct for fluctuations in thefrequency and/or power being transmitted across local and largerelectric interconnected grids.

SUMMARY OF THE INVENTION

Embodiments of the present invention include control methods employed inmultiphase distributed energy storage systems that are located behindutility meters typically located at, but not limited to, medium andlarge commercial and industrial locations. According to such controlmethods, these multiphase distributed energy storage systems willgenerally operate semi-autonomously, but each may be in frequent contactwith a cloud-based optimization engine that is configured to develop andcommunicate various energy control solutions to one or more of thedistributed energy storage systems. One of the goals of an installedmultiphase distributed energy storage system is to monitor thelocation's electric load and electricity use at its specific electricload location, and discharge at times of high demand peaks in order toreduce the peak power provided by the electric grid, while maximizingthe finite amount of energy stored in the consumable energy storagecomponents in the distributed energy storage system. In someembodiments, one or more distributed energy storage systems may be usedto absorb and/or deliver power to the electric grid in an effort toprovide assistance to or correct for power transmission problems foundon the electric grid found outside of an electric load location. Toprovide assistance to or correct problems found on the electric grid,multiple distributed energy storage systems, which are in communicationwith each other and/or an operations center, are used to form acontrolled and coordinated response to the problems seen on the electricgrid.

Embodiments of the invention may provide a system for managing power,comprising a first energy storage device that is in electriccommunication with a first electric power line that is coupled to atleast a portion of an electric power distribution grid, wherein thefirst energy storage device comprises a first energy source, and a firstcontroller that is in communication with the first energy storage deviceand a first sensor configured to measure a frequency of the powertransmitted through the first electric power line, wherein the firstcontroller is configured to control a transfer of energy between thefirst electric power line and the first energy storage device based oninformation generated by the first sensor. The system may furthercomprise a second energy storage device that is in electriccommunication with a second electric power line that is interconnectedto at least a portion of the electric power distribution grid, whereinthe second energy storage device comprises a second energy source, and asecond controller that is coupled to the second energy storage device,and is configured to control a transfer of energy between the secondelectric power line and the second energy storage device, and a gridservices controller that is configured to deliver a control signal tothe first and second controllers, wherein the control signal is based oninformation generated by the first sensor.

Embodiments of the invention may further provide a system for managingpower, comprising a first energy storage device that is in electriccommunication with a first electric power line that is configured totransmit electric power within at least a portion of an electric powerdistribution grid, wherein the first energy storage device comprises afirst energy source and a first controller, a second energy storagedevice that is in electric communication with a second electric powerline that is configured to transmit electric power within at least aportion of the electric power distribution grid, wherein the secondenergy storage device comprises a second energy source and a secondcontroller, and a grid services controller that is configured to receiveinformation relating to a frequency of the power transmitted through thefirst electric power line from a first sensor, and to transfer a controlsignal to the first controller and the second controller wherein thecontrol signal is derived from the information generated by the firstsensor.

Embodiments of the invention may further provide a method of managingpower at an electric load location, comprising monitoring a frequency ofthe power transmitted through an electric power line, wherein theelectric power line is configured to transmit electric power within atleast a first portion of an electric power distribution grid, andcontrolling a transfer of power between a first energy storage deviceand the electric power line based on data received by monitoring thefrequency of the power transmitted through the electric power line.

Embodiments of the invention may further provide a system forcontrolling the transfer of energy between an electric load location andan electric grid comprises a power monitor, an optimization engine, anda system controller. The power monitor is configured to monitor electricpower usage at a point of common coupling with an electric meter and anelectric load at a common location, wherein the electric meter isconfigured to measure power transferred to the electric load locationthrough the electric power line. The optimization engine is configuredto receive one or more external inputs and create a set of operatingparameters based on the one or more external inputs. The systemcontroller is configured to receive the created operating parameters anduse the operating parameters to control an amount of energy flowingthrough the electric power line below a threshold value.

Embodiments of the invention may further provide a system forcontrolling energy transferred between an electric grid and an electricload location comprises an optimization engine and a distributed energysource. The optimization engine is configured to receive one or moreexternal inputs and create one or more operating control curves based onthe one or more external inputs. The distributed energy source comprisesa system controller and a power monitor that is configured to monitor anelectric power line that is coupled to an electric meter, wherein thesystem controller is configured to receive the one or more operatingcontrol curves, compare the one or more operating control curves toinformation received from the power monitor, and control a transfer ofenergy from or to the electric power line from an energy source based onthe computation.

Embodiments of the invention may further provide a method of controllingenergy transferred between an electric grid and an electric loadlocation comprises monitoring a first rate of energy transfer from anelectric power line to the electric load location, wherein the electricpower line is coupled to an electric meter adapted to determine powertransferred between the electric load location and the electric grid,receiving a first set of operating parameters that are created based onone or more received external inputs and, based on the first set ofoperating parameters, varying the energy transfer from an energy sourceto the electric power line to cause the first rate of energy transfer toremain below a threshold value, wherein the threshold value varies withtime.

Embodiments of the invention may further provide a computer readablemedium configured to store instructions executable by a processor of ahost device, the instructions when executed by the processor causing theprocessor to generate control parameters based on a simulation that isperformed using forecast information, monitor a first rate of energytransfer from an electric power line to an electric load location andcontrol a second rate of energy transfer between the electric power lineand an energy source based on the control parameters, whereincontrolling the second rate of energy transfer alters the first rate ofenergy transfer.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentinvention can be understood in detail, a more particular description ofthe invention, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlytypical embodiments of this invention and are therefore not to beconsidered limiting of its scope, for the invention may admit to otherequally effective embodiments.

FIG. 1 illustrates a plurality of distributed energy storage systemsthat are each positioned at an electric load location that isinterconnected to an electrical grid, according to one embodiment of theinvention.

FIG. 2A schematically illustrates one embodiment of a distributed energystorage system that is disposed at an electric load location, accordingto one embodiment of the invention.

FIG. 2B schematically illustrates two electric load locations that areeach interconnected to the electric grid through an electric grid andinclude a distributed energy storage system, according to one embodimentof the invention.

FIG. 2C schematically illustrates a single electric load location thatis interconnected to the electric grid through two electric meters,according to one embodiment of the invention.

FIG. 3 illustrates a process flow diagram for controlling thefluctuation of power at an electric load location and/or power levelbeing drawn by one or more electric load(s) at the electric loadlocation by use of a distributed energy storage system, according to oneembodiment of the invention.

FIG. 4 illustrates the operation method and information flow provided toand/or used by a set-point controller, according to one or moreembodiments of the invention.

FIG. 5 illustrates the operation method and information flow provided toand/or used by a runtime controller, according to one or moreembodiments of the invention.

FIG. 6 illustrates an overview of a communication process between anoptimization engine, a simulator farm, and a distributed energy storagesystem, according to an embodiment of the invention.

FIG. 7 illustrates an overview of a system simulator process, accordingto an embodiment of the invention.

FIG. 8A is a graph that illustrates the performance over a business dayof a prior art energy storage system using a single-demand set-point.

FIG. 8B is a graph that illustrates the performance over a business dayof an energy storage system configured according to one or moreembodiments of the invention.

FIG. 8C is a graph that illustrates the performance over a business dayof an energy storage system configured according to one or moreembodiments of the invention.

FIG. 9 illustrates a control system that includes an optimization engineand is configured to generate operating parameters for an energy storagesystem controller, according to one embodiment of the invention.

FIG. 10 is a block diagram of a general process sequence used by acontrol system to create and deliver forecast and control information toone or more distributed energy storage systems, in accordance with anembodiment of the invention.

FIG. 11 is a block diagram of a process sequence used by a coefficientengine to create updated coefficients for one or more of the distributedenergy storage systems associated with a control system, in accordancewith an embodiment of the present invention.

FIG. 12 is a block diagram of a process sequence used by a forecastengine to generate forward-looking forecasted load profiles for aparticular electric load location serviced by a distributed energystorage system associated with the control system.

FIG. 13 is a block diagram of a process sequence used by a simulationengine to determine optimal set-points and battery curves, according toan embodiment of the invention.

FIG. 14 is a block diagram of a process sequence used by a solutionengine to generate a solution of optimal set-points and battery curvesfor a distributed energy storage system, according to an embodiment ofthe invention.

FIG. 15A illustrates a flat battery curve, according to an embodiment ofthe invention.

FIG. 15B illustrates a stepped battery curve, according to an embodimentof the invention.

FIG. 15C illustrates a continuously varying battery curve, according toan embodiment of the invention.

FIG. 16 is a block diagram of a process sequence used by an optimizationengine to generate and distribute an optimal battery curve for adistributed energy storage system, according to an embodiment of theinvention.

FIG. 17 is a graph illustrating an example of a corrected electric gridfrequency excursion event versus time, according to one embodiment ofthe invention.

FIG. 18 illustrates a plurality of distributed energy storage systemsthat are interconnected to different regions of an electrical grid,according to one embodiment of the invention.

FIG. 19 is a block diagram of a process sequence used to correct adetected electrical grid event, according to one embodiment of theinvention.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures. It is contemplated that elements disclosed in oneembodiment may be beneficially utilized on other embodiments withoutspecific recitation. The drawings referred to here should not beunderstood as being drawn to scale unless specifically noted. Also, thedrawings are often simplified and details or components omitted forclarity of presentation and explanation. The drawings and discussionserve to explain principles discussed below, where like designationsdenote like elements.

DETAILED DESCRIPTION

Embodiments of the present invention include control methods employed inmultiphase distributed energy storage systems that are located behindutility meters typically located at, but not limited to, medium andlarge commercial and industrial locations and/or are connected alongvarious parts of an electric grid. These distributed energy storagesystems can operate semi-autonomously, but each may be in frequentcontact with a cloud-based optimization engine that is configured todevelop energy control solutions based on various data inputs and tocommunicate these energy control solutions to the distributed energystorage systems. Each installed distributed energy storage system can beused to monitor or receive information about the electricity use at itsspecific location and information relating to the power transmission onthe electric grid. In an effort to control the local demand, thedistributed energy storage system can discharge at times of high demandpeaks in order to reduce the peak power provided by the electric grid,while maximizing the finite amount of energy stored in the consumableenergy storage components in the distributed energy storage system. Toperform this task the distributed energy storage system may control thekilowatt demand of the local electric load location by controlling thecontrol set-point over time by discharging energy sources, such as DCbatteries, through one or more bidirectional power converters. Thesesystems generally recharge at times of low demand and/or low electricityrates. From the utility's perspective, demand spike reduction is morevaluable than the act of consuming additional energy during periods oflower demand.

In some embodiments, one or more distributed energy storage systems maybe used to absorb and/or deliver power to the electric grid in an effortto provide assistance to or correct for power transmission problemsfound on the electric grid outside of an electric load location. Toprovide assistance to or correct problems found on the electric grid, aplurality of distributed energy storage systems, which are incommunication with each other and/or an operations center, are used toform a controlled and coordinated response to the problems seen on theelectric grid.

Distributed Energy Storage Systems and Demand Control Solutions

FIG. 1 illustrates a plurality of distributed energy storage systems 103that are each positioned at an electric load location 104 that iscoupled, or connected, to an electrical grid 102, according to oneembodiment of the invention. The electrical grid 102 will generally beconnected to one or more electric load locations 104 and one or morepower plants 102A that are adapted to deliver electric power to theelectric grid 102. In general, an electric utility will help provideand/or deliver power to each of the electric load locations 104 in aregion of the electric grid 102. In some cases, the tariff structure,such as electric rates and billing schedules, for different electricutilities may vary from region to region within the electric grid 102.The distributed energy storage systems 103, also referred to as advancedenergy systems (AESs), are coupled to the electric grid 102.Consequently, the distributed energy storage systems 103 may be incommunication with other distributed energy storage systems 103distributed along the electric grid 102 and may be in communication withan operations center 109. The operations center 109 may include softwareand hardware components that are configured to store, retrieve operationinformation from, and transmit operation information to each distributedenergy storage system 103 to control the power fluctuations and powerdelivery at each respective electric load location 104. In some cases,the operation information may include environmental data, control setpoint information, device commands and other useful information.Distributed energy storage systems 103 in the different regions of thegrid are generally able to account for differences in power pricing(including energy tariffs and real-time energy pricing), differences inweather, differences in the health of the electric grid, and otherexternal and internal electric power usage differences to provide anoptimized and/or customized control of the power at each electric loadlocation 104.

Embodiments of the invention use a control method employed in thedistributed energy storage systems 103 located behind a utility'selectric meter 201 (FIGS. 2A-2C) typically located at, but not limitedto, medium and large commercial and industrial locations. FIG. 2Aschematically illustrates one embodiment of a distributed energy storagesystem 103 that is disposed at an electric load location 104. Thedistributed energy storage system 103 may include a power monitor 230,one or more power controllers 222, which are coupled (or connected) toan energy source 224, and a system controller 210. The electric loadlocation 104 typically contains an electric meter 201 that is coupled tothe electric grid 102 and is used by the utility to track electricityusage at the electric load location 104. The electric meter 201 isconfigured to provide power to one or more electric loads that areconnected to a breaker panel 240 (e.g., three electric loads 241A-241Care shown in FIG. 2A).

In one example, the electric meter 201 is configured to distribute powerto the electric loads 241A-241C along one or more phases that are eachcoupled to the breaker panel 240 along a conducting element 235. Ingeneral, an electric load can be any device that uses electrical energyat an electric load location 104, and may include, for example, heating,ventilation, air conditioning (HVAC) equipment, lighting, and otherelectronics units that receive power from the electric grid 102. Eachelectric load 241A-241C may separately draw power through eachconducting element 235. The amount of power passing through theconducting element 235 is monitored by a sensor 234 disposed in thepower monitor 230. The power monitor 230 will typically include one ormore sensors 234 (e.g., voltage sensor and/or current sensor) that areconfigured to monitor and deliver a signal to a power monitor controller232 that is configured to process and deliver data relating to the timevarying current (A), voltage (V) and/or power (W) delivered on the oneor more phases to the system controller 210, and in some cases timevarying current, voltage and/or power data to the operations center 109.In general, the power monitor 230 can be used to measure powertransferred through one or more electric power lines at the electricload location 104, wherein the act of measuring the power transferredbetween the one or more electric power lines and the distributed energystorage system 103 may include either measuring current (A), voltage (V)and/or power (W).

To control fluctuation in power and/or power level being drawn by eachof the electric loads 241A-241C in an electric load location 104, thedistributed energy storage system 103 typically includes one or morepower controllers 222 that are configured to control the delivery ofpower to the electric grid 102 or absorption of power received from theelectric grid 102 by use of a connected energy source 224. In oneembodiment, the power controllers 222 include one or more bidirectionalpower converters 225 (shown in FIGS. 2B and 2C) that are capable ofquickly converting stored DC energy found in the energy source 224 tothe grid AC electricity and grid AC electricity back to DC energy thatis stored in the energy source 224. An example of a bidirectional powerconverter that may be adapted for use with the distributed energystorage systems disclosed herein is further described in the commonlyassigned U.S. patent application Ser. No. 12/731,070, which was filedMar. 24, 2010, which is herein incorporated by reference.

The distributed energy storage systems 103 can operate autonomously, butgenerally may be in frequent contact with a cloud-based optimizationengine that may be located in the operations center 109. Theoptimization engine 1031, which is discussed further below, can take invarious data inputs and develop optimal energy control solutions whichare passed back down to one or more of the distributed energy storagesystems 103. In most cases, the primary goal of the installeddistributed energy storage system 103 is to keep kilowatt demand of theelectric load location 104 from exceeding certain set-point(s), whichmay be altered at different times of the day. Simply, this occurs bydischarging the energy stored in the energy source 224, such as energystorage devices that may include DC batteries, through the bidirectionalpower converter 225 during peak demand events. In some embodiments, thedistributed energy storage systems 103 also manage the batterystate-of-charge by recharging energy source 224 during periods of lowerdemand. The state-of-charge represents the amount of energy stored inthe storage medium of energy source 224 (e.g., batteries), which can beconverted to electrical energy at any time of day, for example during apeak demand event. The distributed energy storage system 103 isgenerally intelligent enough to ensure that there is adequate energystored in the energy source 224 to be able to offset at least a portionof high-demand events. Also, the controlling elements (e.g., systemcontroller 210) in the energy storage system 103 can be used to preventthe unwanted exhaustion of the stored energy in the energy source 224during non-high demand times, due to an unscheduled or unforeseen demandduring these non-critical and low energy cost times. Thus, employing acontrol system that is based on predictive data can reduce total energycost to the customer and/or to help make the wider electric gridcleaner. Therefore, energy storage systems that can predict theoccurrence of peak demand events allow the energy storage system tobetter manage the state-of-charge of the energy storage medium in theenergy source 224, and maximize the amount of time that the energystorage components are available to be used to reduce demand spikesduring a day, and especially demand spikes during times of high cost andhigh-demand on the grid.

The system controller 210 typically includes a central processing unit(CPU) (not shown), memory (not shown), and support circuits (or I/O)(not shown). The CPU may be one of any form of computer processor thatis used for controlling various system functions and support hardwareand monitoring the processes being controlled by and within thedistributed energy storage systems 103. The memory is coupled to theCPU, and may be one or more of a readily available memory, such asrandom access memory (RAM), read only memory (ROM), floppy disk, harddisk, or any other form of digital storage, local or remote. Softwareinstructions (or computer instructions) and data may be coded and storedwithin the memory for instructing the CPU. The software instructions mayinclude a program that determines which tasks are to be performed at anyinstant in time. The support circuits are also connected to the CPU forsupporting the processor in a conventional manner. The support circuitsmay include cache, power supplies, clock circuits, input/outputcircuitry, subsystems, and the like. The system controller 210 isconfigured to receive information from and deliver control commands tothe source power controller 223 found in the one or more powercontrollers 222 via a wired or wireless communication link 223A. Thesystem controller 210 is also configured to receive information from thepower monitor 230 via a wired or wireless link 209 and from theoperations center 109 via a wired or wireless link 109A.

In one embodiment, the system controller 210 also includes a pluralityof software based controlling elements that are adapted to synchronizeand control the transfer of power between the conducting element 235that is interconnected to (or electrically coupled to) the electric grid102 based on computer instructions retained in the memory of the systemcontroller 210. The software based controlling elements found in thesystem controller 210 include a solution manager 202, a set pointcontroller 204, and a run time controller 206. In some embodiments, thesoftware based controlling elements may also include an optional offsetcontroller 208 and/or an local power controller 207.

FIG. 2B schematically illustrates two electric load locations 104 thatare each interconnected to the electric grid 102 through an electricmeter 201 and include a distributed energy storage system 103. In thisexample, each of the distributed energy storage systems 103 are incommunication with each other through a link 109B. In one embodiment,the link 109B is created using wired or wireless communicationcomponents found in the power monitor controller 232, so that thecontrol between electric load locations 104 can be coordinated. Forclarity, the electric loads 241A-241C are not show in the distributedenergy storage systems 103 of FIG. 2B.

In one embodiment, as illustrated in FIG. 2B, the distributed energystorage systems 103 each include one or more power controllers 222 thatare interconnected to an energy source 224 and include a source powercontroller 223 and a bidirectional power converter 225. The energysource 224 may include one or more batteries 227 that may be coupled inseries, so as to provide a desirable output voltage (Volts) to thebidirectional power converter 225 and provide a desirable storagecapacity (Amp-hrs). The bidirectional power converter 225 may includeone or more software and/or hardware components (e.g., bridgerectifiers, power transistors, transformers, resistors, capacitors,diodes) that are capable of controlling the delivery and absorption ofpower from the electric grid 102. In some embodiments, the bidirectionalpower converter 225 may include components that are able to separatelydeliver power to or receive power from a conducting element 235 that isseparately connected to a phase that is interconnected to the electricgrid 102. In such embodiments, the energy source 224 may include aplurality of separate battery arrays (not shown) that are each coupledto a separate power controlling circuit in the bidirectional powerconverter 225 to control the efficient transfer of power at a desirablerate between the conducting element 235 and energy source 224. Suchbattery arrays may include two or more batteries 227 each.

FIG. 2C schematically illustrates a single electric load location 104that is interconnected to the electric grid 102 through two electricmeters 201, according to one embodiment of the invention. Each of theelectric meters 201 are configured to separately or cooperativelyprovide power billing information to the utility, due to the electricpower drawn by the loads 241A-241F. As illustrated in FIG. 2C, a singledistributed energy storage system 103 can be configured to control thefluctuation in power at the electric load location 104 and/or powerlevel being drawn by a plurality of electric loads (e.g., electric loads241A-241F) at the electric load location 104. In this configuration, thedistributed energy storage systems 103 may include two or more powermonitors 230, one or more power controllers 222 and a system controller210. The bidirectional power converter 225 may also include componentsthat are able to separately control the transfer of power between aconducting element 235, electric grid 102, and the energy source 224.

FIG. 3 illustrates a process flow diagram for controlling thefluctuation of power at an electric load location 104 and/or power levelbeing drawn by one or more electric load(s) at the electric loadlocation 104 by use of a distributed energy storage system 103,according to one embodiment of the invention. In general, the softwareand hardware components in the system controller 210 (shown in FIGS.2A-2C) are configured to provide control-based commands to control theone or more power controllers 222 based on the measured power datacollected by power monitors 230. The overall control system of thedistributed energy storage system 103 is made up of several software andhardware components that may be distributed across the local site (e.g.,electric load location 104) and/or the Internet cloud. FIGS. 2A-2Cdiscussed above illustrate some examples of different configurations ofthese components. The electric load location 104 contains hardwareresponsible for implementing the control system, including charging thebatteries 227 from and discharging the batteries into the electric grid102. Monitoring capability may be included in the bi-directionalconverter in the source power controller 223 that passes the measuredcharge and discharge information back to the system controller 210 inthe local distributed energy storage system 103. Additionally, there maybe a separate sensor (e.g., a monitoring device, also referred to hereinas the power monitor 230) that monitors the overall building load (thatis, the net of any charging or discharging) and also passes thisinformation back to the system controller 210 in the distributed energystorage system 103.

The system controller 210 of the distributed energy storage system 103may include up to six primarily software-based controllers that arestored within memory and executed by one or more processors associatedwith the system controller 210. As noted above, these six primarilysoftware-based controllers may include the solution manger 202, theset-point controller 204 (described below in conjunction with FIG. 4),the run time controller 206 (described below in conjunction with FIG.5), the local power controller 207, the offset controller 208, a droopcontroller 330, and an optimization engine 1031 (described below inconjunction with FIGS. 7 and 9). The various controllers in the controlsystem, as noted above, will typically include a central processing unit(CPU) (not shown), memory (not shown), and support circuits (or I/O)(not shown). Software instructions and data can be coded and storedwithin the memory of one or more of the controllers and is used forinstructing the CPU of the one or more of the controllers to control thevarious components in the distributed energy storage system 103.

The solution manager 202 exists on the local premise and is primarilyresponsible for communicating with the optimization engine 1031 andpassing command information received therefrom to the set-pointcontroller 204 and offset controller 208. In one embodiment, thesolution manager 202 receives demand threshold control instructions(e.g., demand set-point curves 301) and battery state-of-charge curves302 from the optimization engine 1031 and then determines at whattime(s) of day the set-point controller 204 changes the demandset-point. For example, if during a period of low demand charges (e.g.,energy cost is low), the solution manager 202 receives new informationthat a high demand charge period (e.g., energy cost is high) isapproaching, the solution manager 202 passes along the new controlset-point to which the set-point controller 204 should attempt to holdthe premise load at that time. There are several events the solutionmanager 202 watches for in order to make changes to the set-point,including a soft failure event, a soft failure set point incrementevent, a manual set-point change event, a new solution receivedset-point update event, a set-point controller startup event, and a PIDcoefficient change event.

A soft failure event indicates that operating conditions of thedistributed energy storage system 103 have gone outside the forecastedoperating solution, or the demand has exceeded its predicted operatingvalue(s) due to an unexpected variation in demand. This type of event istypically informational and allows the optimization engine 1031 todecide if intervention is required. A soft failure set point incrementevent, which can be either incremental or proportional, indicateswhether a set point change is needed as a result of a soft failureevent. Set point modifications are either incremental or proportional tothe set point and utility and site loads. If the solution manager 202decides that the correction to the soft failure should be incremental,then the set point will be adjusted upward based on the incrementalvalue found in the forecasted battery curve layer for the currentstate-of-charge (SOC). If the solution manager 202 decides that thecorrection to the soft failure should be proportional, then the setpoint will be adjusted up or down based on the proportional differencebetween the utility and resulting load. Much like the incremental value,the proportional value used to correct for the soft failure event isfound in the battery curve layer of the controlling software found inthe solution manager 202 and is based on the current state-of-charge ofthe energy source 224. A manual set-point change event is recorded whena user manually changes the set point on the distributed energy storagesystem 103. A new solution received set-point update event indicates theset point is updated based on a newly received solution from theoptimization engine 1031. This will generally occur when the distributedenergy storage system 103 transitions from one billing cycle to anotheror when intervention by the user causes a regeneration of a newsolution. A set-point controller startup event indicates that theset-point controller 204 was restarted, generally due to manualintervention of a user or when the distributed energy storage system 103is restarted. A PID coefficient change event indicates that the PIDcoefficients, used in the run time controller 206, have been updated,for example to accommodate dynamic battery state-of-health (SOH)strategies.

The set-point controller 204 is used to manage the control set-point foreach instant in time using information specified and received from thesolution manager 202. As noted above, the control set-points areselected by the solution manager 202 to ensure that the system maintainsenough energy reserve in the energy source 224 to manage the load at theelectric load location 104 over a particular time period, such as a dayor part of a day. FIG. 4 illustrates operation of set-point controller204, according to one or more embodiments of the invention.

As shown in FIG. 4, in some embodiments, the set-point controller 204receives the optimized runtime parameters 401 from the solution manager202 and demand set-point curves 301 and battery state-of-charge profiles302 from optimization engine 1031. Simultaneously, set-point controller204 monitors the actual operating characteristics of energy storagesystem 103, including for example information 402 from power monitor 230and battery telemetry 403 (e.g., battery SOC as a function of time) frompower controller 222, and sends updated runtime parameters 410 (e.g.,set-points, PID parameters) to the runtime controller 206. In someembodiments, set-point controller 204 also monitors transport delayinformation and other inverter telemetry 404 (e.g., power deliveryinformation) from bidirectional power converters 225 or any otherinverter/chargers associated with energy storage system 103.

The updated control parameters 410 are based on the received optimizedoperating parameters and/or the current operating state of the measuredpower being drawn by the attached electric loads at the energy storagesystem 103. The set-point controller 204 does this by receiving currentreal-time demand information from the observed site load monitor (e.g.,power monitors 230), receiving real time battery state-of-chargeinformation directly from hardware monitoring components (e.g.,charge/discharge monitor 323), which may be found in the source powercontroller 223 in the power controllers 222, receiving battery telemetry403 (e.g., real time charge and discharge information from thecharge/discharge monitor 323), and then issuing commands (updatedruntime parameters 410) to the run-time controller 206. The run-timecontroller 206 controls the charge or discharge of energy to or from theenergy source 224 and to or from the electric grid 102, via thebi-directional power converter 225.

If the actual operating characteristics of the distributed energystorage system 103 fall outside of the expected operating parameters,the set-point controller 204 will adjust the updated runtime parameters410 to correct for any variance from the actual or expected performanceof the overall system. In some embodiments, set-point controller 204 isalso configured to pass any variance information 411 back to theoptimization engine 1031 so that the optimization engine 1031 cancontinue to run the most accurate simulations and issue newly optimizedoperating parameters.

The runtime controller 206 uses the updated runtime parameters 410received from the set-point controller 204 to implement an optimizedcharging or discharging solution for the energy storage system 103. FIG.5 illustrates the operation of the runtime controller 206 according toone or more embodiments of the invention. As shown, the runtimecontroller 206 generally receives the updated runtime parameters 410from the set-point controller 204 and uses a feedback controller (e.g.,local power controller 207), such as a PID controller, to implement theoptimized charging or discharging solution determined by the set-pointcontroller 204 utilizing the power electronics hardware in thebidirectional power converter 225. The runtime controller 206 acts onthe current demand set-point data, which may be included in updatedruntime parameters 410, and sends the actual charge and dischargeinstructions 501 to the bidirectional power converter 225 using standardcontrol mechanisms, such as proportional integral and derivative (PID)types of control loops. The data received by runtime controller 206 mayinclude the current demand set-point curves 301 generated by theoptimization engine 1031 and/or solution manager 202. The runtimecontroller 206 then compares the received inputs and supplies a controlsignal (charge and discharge instructions 501) to the bidirectionalpower converter 225, so that a desired amount of energy is received ordischarged at that instant in time.

Based on charge and discharge instructions 501, fluctuations in thepower used by the electric load location 104 can be controlled or dampedby the charging and discharging of the energy source 224, such as abattery array in the energy source 224, that is coupled to thepower-receiving portion of the electric load location 104. The energysource 224 may be configured to deliver and/or receive an amount ofenergy at any instant in time to and/or from the A/C power system of theelectric load location 104. The energy source 224 may include aplurality of batteries (e.g., lithium ion batteries, lead acidbatteries, etc.) that are electrically interconnected to a portion ofthe A/C power system of the electric load location 104. The plurality ofbatteries may be connected in a series and/or a parallel configurationto the A/C power system. The charging and discharging of the energysource 224 can be controlled by use of the power electronics hardware inthe bidirectional power converter 225, which may include A/C switches,diodes, capacitors and/or inductors. In some embodiments, the runtimecontroller 206 is configured to constantly send the actual charge anddischarge instructions 501 to the power controllers 222 while monitoringcharging and discharging behavior of the energy source(s) 224 in aclosed control loop using a charge/discharge monitor, such as thecharge/discharge monitor 323 in FIG. 3.

The energy source 323 includes one or more sensors that are adapted tomonitor the state-of-charge of one or more energy source components,such as batteries, found within each energy source 224. In one example,the energy source 323 is able monitor the state-of-charge of eachbattery array and/or each battery within a battery array to determineits relative health and state-of-charge (e.g., amount of energy storedin the batteries). In some embodiments, the charge/discharge monitor 323is configured to deliver charging and discharging behavior information(e.g., battery telemetry data) to the runtime controller 206 andset-point controller 204, so that the current demand set-point data usedby the runtime controller 206 can be updated and the set-point at eachinstant in time is the better managed using commands sent from theset-point controller 204.

The local power controller 207 is used to execute the commands that arereceived from the runtime controller 206, and thus execute the actualcharge and discharge processes controlled by the components in thebidirectional power converter 225. The local power controller 207 mayinclude various standard control mechanisms, such as PID control loops,and may be run using a processor found within the system controller 210(FIG. 2A) or power controllers 222 (FIG. 3).

The optional offset controller 208 is used to execute grid servicescommands that are received by the electric load location 104, and isused as a higher-level system override of the control provided by thesystem controller 210. The offset controller 208 may be configured tomodify the charge or discharge command to the bidirectional powerconverter 225 and the telemetry information, such as current load, fromthe resultant load monitor (e.g., sensor power monitors 230). The offsetcontroller 208 thus adjusts the power absorbed or delivered to theelectrical grid 102 by the energy source 224 based on instructionsreceived by the system controller 210 to help resolve electrical issuesthat may be occurring on the grid 102 at that time (e.g., voltagesupport, frequency regulation, etc.). For example, if the solutionmanager 202 calls for a grid services event that calls for an additional2 kW of power, the offset controller 208 would modify the dischargecommand from the runtime controller 206 to increase by 2 kW of power,but would also add that 2 kW of power back to the telemetry of the powermonitors 230 so that the set-point controller 204 and the optimizationengine 1031 would not see a deviation from the predicted load andthereby not disturbing the optimized instruction set that is currentlybeing executed.

The droop controller 330 takes frequency measurements from a sensor 310and a device 331 that are coupled to the electric grid 102 via a wiredor wireless communication link 311. This frequency is compared to thenominal grid frequency for the operating region, for example in theUnited States it would be compared to 60.000 Hz. When commanded to be infrequency regulation mode from the distributed system gateway 325 (FIG.3), if the frequency of the grid is below the nominal frequency thedroop controller 330 will adjust the offset controller 208 (as shown inFIG. 3) to so that commands to the energy source 224 are altered toincrease the discharge rate within predetermined deadbands andtimedelays. Alternatively, when the grid frequency is measured to begreater than 60.000 Hz the commands to the energy source 224 can bealtered to store energy at a greater rate than required for local demandreduction. The response characteristics, such as the speed and magnitudeof the response, are parameters that are controlled by the distributedsystem gateway 325. The distributed system gateway 325 can be acloud-based interface and controller that allows for specific gridservices commands to be sent to the distributed energy storage system103 outside of the control of optimization engine 1031. Thus, the droopcontroller 330, much like the optional offset controller 208, can beused as a higher-level system override of the control provided by thesystem controller 210.

In operation, the control method used to maintain the required demandset-point(s) can employ a proportional-integral-derivative (PID)controller that heavily relies on the information received from anoptimization engine 1031. In some embodiments, the control method uses astandard PID control loop, where the process value is the observedelectric load at the local premise, the set-point is the demand inkilowatts that the storage system attempts to prevent the local loadfrom exceeding, and the manipulated value is the amount of energydischarged by the bidirectional power converters 225. In simple terms,if the local load is sensed or observed to exceed the demand set-point,then the bidirectional power converter 225 receives a command todischarge until the observed electric load begins to drop below thedemand set-point. This control loop may occur at a frequency from 100milliseconds or less to multiple seconds depending on load andvolatility, and on the granularity of the control output of thebidirectional power converter 225. For example, said frequency may varyas a function of the ability of the bidirectional power converter 225 tocontrol output, e.g., within 10's of watts, hundreds of watts, etc.Given this control output granularity and speed, the control system 210can react within a suitable response time to sensed changes in theelectric load sensed by power monitor 230.

In some configurations, the distributed energy storage system 103 iscomputing resource constrained. Therefore, the generation of the controloptimization instructions occurs at an external location (e.g.,operations center 109) and then a distilled set of instructions ispassed down to one or more of energy storage systems 103 from theexternal location. The predictive nature and/or processes found in thecontrol optimization instructions is particularly beneficial, sincethese control optimization instructions enable a minimized or otherwisereduced amount of energy storage to be used at the premise. Since theenergy storage devices, such as the energy source 224, can be expensiveand have a finite lifetime that is affected by amount of use, the systemcontroller 210 can be used to further improve the lifetime of the energysource 224 and thus reduce the operating costs of distributed energystorage system 103. For example, if the optimization engine 1031forecasts that the maximum peak load for the day will occur at 4 PM, theinstruction set includes this forecast, so that the distributed energystorage system 103 does not overreact to spikes in demand prior to 4 PM,and therefore enough energy capacity will remain in the energy source224 for the major 4 PM event. If an unpredicted event occurs prior to 4PM, the distributed energy storage system 103 will still react correctlyand hold the set-point. This unexpected event information is sent backto the optimization engine 1031, where an updated set of instructions iscreated for the storage system, which may include a higher set-pointlater in the day so the energy source 224's state-of-charge is notdepleted.

According to embodiments of the invention, distributed energy storagesystem 103 maximizes economic return by optimally managing energy use atthe electric load location 104. Rather than simply reacting to grosschanges in electricity demand, distributed energy storage system 103 isconfigured to use a partially predicted command set that may bestatistically derived from a large quantity of various data inputs. Inpractice, some embodiments of distributed energy storage system 103 maylack the processing power, data storage capacity, and/or interfaces toremote data stores that facilitate calculating such a partiallypredicted command set locally in a timely manner. Consequently, in suchembodiments, the partially predicted command set may be calculated at aremote location and the information distilled to a lightweight, i.e.,easily transmitted, set of instructions that the distributed storagesystem 103 can utilize in real-time. For example, in some embodiments,optimization engine 1031 is configured to calculate the command set atsuch a remote location (e.g., operations center 109 in FIGS. 2A-2C) andthen deliver the command set to the energy storage system 103.

Additionally, in some embodiments, the created command set may include apredictive element, as opposed to simply a fixed set point, so that thedistributed energy storage system 103 can operate autonomously for sometime (e.g., hours or days) in the event that the distributed energystorage system 103 is disconnected from the central controller and isunable to communicate with a central command center (e.g., operationscenter 109). For example, disconnection can occur if the localcommunications network is disconnected for maintenance or if damageoccurs to the communication wiring. In such a situation, the predictiveelement of the command set facilitates resilient operation ofdistributed energy storage system 103 with respect to various futureconditions, such as demand spikes or changes in rate schedule. Thus, insome embodiments, distributed energy storage system 103 is configured tooperate using a robust, but small (in terms of data size) command setthat is received from a central command center (e.g., from optimizationengine 1031 at operations center 109) and that allows autonomousoperation (via a predictive element) until communications arereconnected with the central command center. In other words, embodimentsof the invention can be used to create a concise set of operatingparameters in an external data center that the distributed energystorage system 103 can understand and implement while the distributedenergy storage system 103 is either connected or disconnected from thecommunications network, which couples the external data center and thedistributed energy storage system 103. These operating parametersinclude a statistically generated and tiered expected state-of-charge ofthe battery or batteries in the distributed energy storage system 103(e.g., the battery state-of-charge profiles 302 in FIGS. 4 and 5) aswell as a set of set points at which to maintain the electric powerdemand at the load site to enable the forecasted economic return (e.g.,the time-based demand set-point curves 301 in FIGS. 4 and 5). Examplesof the battery state-of-charge profiles 302 and their uses are furtherdiscussed below in conjunction with FIGS. 10-14 and 15A-15C.

Thus, in some embodiments, a process is provided in which operatingparameters are computed through simulations and other statisticaltechniques in a remote data center. These data are then distilled downinto a lightweight set of storage system operating parameters (e.g., inthe several kilobytes size range), which are acted upon by thecontrolling elements in the energy storage system 103, and then anychanges in operating characteristics are communicated back to thecentral data center (e.g., operations center 109) for continualoperating parameter optimization.

The optimization engine 1031 may exist in the cloud 104A, as shown inFIG. 3, or alternatively at an electric load location 104, and buildsthe optimal operating instructions for one or more of the distributedenergy storage systems 103 by running simulations using multiplecategories of data, which may include: electric rate plan and tariffinformation (e.g., utility rates, billing period, temporal energy anddemand prices, and averaging periods), utility data (e.g., requests forgrid services like voltage support and frequency regulation), currentand future weather data, (e.g., time of day, sunrise and sunset times,temperature, cloud cover, and other weather forecasts), geographiclocation, local solar production, local incident light, customer type,building specifications (e.g., information relating to the electric loadlocation, such as building material, square footage, age, HVAC type andtype of equipment used at the electric load location, etc.), gridoperator data (e.g., locational marginal price, area control error,automatic generation control, etc.), and time (e.g., time of day) data,high-resolution energy usage data (e.g., collected high sample rateenergy usage data (e.g., <1 minute)) provided by the target energystorage system, and/or load prediction taken from historical patternsfor the specific site and similar sites, which may be received from oneor more external data sources. Optimization engine 1031, which may beconfigured as a software-based analytics engine, then uses this data tosimulate and create various operating parameter results by iterativelyprocessing this data to find an optimal solution. The operatingparameter results so determined may include optimized runtime parameters401, demand set-point curves 301, and battery state-of-charge profiles302 of FIGS. 4 and 5. For example, in some embodiments, optimizationengine 1031 uses an energy storage hardware simulator farm asillustrated in FIG. 6 to help determine and create various operatingparameters.

FIG. 6 illustrates an overview of a communication process between anoptimization engine 1031, a simulator farm 601, and a distributed energystorage system 103, according to an embodiment of the invention. Thesimulation farm 601 may be located in a data center external to thedistributed energy storage systems 103 and, as shown, may be configuredwith a plurality of system simulators 602 to run multiple simulationsusing the various categories of data listed above as input variables.Each simulation can then produce operating parameters and storage systemresults (e.g., optimized runtime parameters 401, demand set-point curves301 and battery state-of-charge profiles 302) for a particulardistributed energy storage system 103, including energy use predictionsthat are used by the semi-autonomous control system of each distributedenergy storage system 103. Simulator farm 601 may be hosted in the samecloud-based system or computer as the optimization engine 1031, or maybe associated with a dedicated cloud or computer or other computingdevice.

After completing a simulation to compute the optimal power controlstrategy for a distributed energy storage system 103, the optimizationengine 1031 then packages and passes down an array of time-based demandset-point curves 301, battery state-of-charge profiles 302, andoptimized runtime parameters 401 to the set-point controller 204 of thedistributed energy storage system 103, which then acts upon them. Demandset-point curves 301 provide a series of time-based power level demandset-points, so the distributed energy storage system 103 knows whatkilowatt set-point to hold the local load(s) (e.g., electric load(s)241A-241C in FIGS. 2A-2C) to during different times of day. The batterystate-of-charge profile 302 provides a set of zones that the set-pointcontroller 204 will act on if the current state-of-charge moves out ofthe safe zone at different times over a desired time period (e.g., aday). The system controller 210 uses this information so that the powercontrol strategy can be adjusted if the battery state-of-charge movesoutside a predetermined safe state-of-charge level, due to unpredictedchanges in the load at the electric load location 104.

The strategy computed by the optimization engine 1031 may also includegrid services instructions that control the offset controller 208 viathe solution manager 202. Grid services may include frequencyregulation, voltage support, and demand response commands, whichtypically do not disrupt the control loop that is optimizing the powerprovided to local premise load(s), and are used to allow the distributedenergy storage system 103 to help support and resolve issues that ariseon the greater electrical grid, which is outside of the commercialelectric load location 104.

As described above in conjunction with FIGS. 2A-2C, the set-pointcontroller 204 unpackages the operating parameters and uses this data todetermine a runtime plan, e.g., updated runtime parameters 410, as aguideline for operation of distributed energy storage system 103. Insome embodiments, set-point controller 204 may also be configured toreport back to the optimization engine 1031 any variances in expectedbehavior using runtime variance 610 data, based on the actual behaviorof distributed energy storage system 103, e.g., system telemetry 620.The optimization engine 1031 can then use runtime variance 610 to runanother set of simulations using one or more system simulators 602 inorder to compute a new optimal solution that accounts for thesevariations at each energy storage system 103.

FIG. 7 illustrates an overview of a system simulator process, accordingto an embodiment of the invention. An individual system simulator 602may be configured to behave exactly like a particular distributed energystorage system 103, but is entirely software-based. A system simulator602 takes in the operating parameters produced by the optimizationengine 1031 as well as the utility's electric usage billing period andpasses this information into a feedback controller that simulates thecontrol loop of a particular distributed energy storage system 103. Inone embodiment, the system simulator 602 generates data in sub-second orgreater increments. However, the utility that delivers the electricalpower typically measures for demand peaks in longer multi-minute timeblocks. Therefore, in some embodiments, the simulation time periodoptimizations are matched to the site's utility billing time periods. Ifthe operating parameters are optimized for a time significantly shorterthan the utility billing time period associated with a particulardistributed energy storage system 103 being simulated, morecomputational resources than necessary may be used to reach a desiredsolution. Alternatively, if the operating parameters are optimized for atime significantly longer than the utility billing time periodassociated with a particular distributed energy storage system 103 beingsimulated, a demand spike may be missed.

As noted above, optimization engine 1031 can be used to create a conciseset of operating parameters (including time-based demand set-pointcurves 301, battery state-of-charge profiles 302, and optimized runtimeparameters 401) that the distributed energy storage system 103 canunderstand and implement. Furthermore, these operating parameters can beused by the distributed energy storage system 103 even if subsequentlydisconnected from the communications network coupling the optimizationengine 1031 and the distributed energy storage system 103. The systemcontroller 210 of the distributed energy storage system 103 uses thetime-based demand set-point curves 301 and battery state-of-chargeprofiles 302, collectively known as feedback based curves, to controlthe storage and release of energy by the distributed energy storagesystem 103. These battery state-of-charge profiles, or battery curves,can be used for a single battery, a group of connected batteries in asingle storage device, or a group of batteries contained in severalnetworked energy devices, such as in the energy source 224. A batterystate-of-charge profile 302 represents a targeted state-of-charge of thebattery or battery array as a function of time and will vary in theamount of power correction from 0% to 100%. A time-based demandset-point curve 301 represents one or more peak demands that the siteshould not exceed over the course of a specified time period, e.g., oneday. There may be multiple such demand peaks during a particular day,and each may be defined by power as a function of time of day. Thebattery state-of-charge profile, or battery curve, can be used by thesystem controller to determine an amount of energy to transfer to theelectric power line (e.g., conducting element 235) based on a measuredamount of charge in the energy source 224.

The time-based demand set-point curves 301 and battery state-of-chargeprofiles 302 can be transmitted either as a matrix or table oftime-indexed values, or as a time-varying equation. The points definingtime-based demand set-point curves 301 and battery state-of-chargeprofiles 302 are synchronized in time and are time-delineated inrelatively short time increments (such as one minute intervals). Thematrix or equation that allows the system controller 210 to build thebattery state-of-charge profiles 302 may be in a format of batterystate-of-charge percentage with respect to time. Such a matrix orequation allows the system controller 210 to build the demand set-pointcurve, which comprises electric power (in kilowatts, for example) withrespect to time.

During operation, the system controller 210 attempts to maintain thegiven set-points as time progresses by monitoring power delivered to andfrom the electric load location 104 through a power delivery line (e.g.,one or more conducting elements 235) coupled to electric meter 201, andstoring and releasing energy to the delivery line from a storage device,such as the energy source 224. The battery state-of-charge profiles 302received by the system controller 210 provides a guide for whatstate-of-charge is expected to be for the distributed energy storagesystem 103 in order to maintain the demand set-points. If the actualstate-of-charge deviates significantly from the expectedstate-of-charge, the system controller 210 will adjust the demandset-point and report the variance back to the optimization engine 1031.

According to some embodiments, a set of operating parameters provided todistributed energy storage system 103 by optimization engine 1031 mayinclude more than a single demand set-point curve 301 and a singlebattery state-of-charge profile 302 (or battery curves). In someembodiments, these operating parameters may includes a matrix ofsynchronized battery and demand set-point data, including “default,”“optimized,” and “current” curve sets. The simplified default curve setis calculated at system installation and initial setup of systemcontroller 210. This set includes a simple demand set-point curve 301and a simple battery state-of-charge profile 302 that can be used by thesystem controller 210. The optimized battery curve set is calculated bythe optimization engine 1031 at the beginning of the billing period(e.g., once per month) or when changes occur in energy and power utilityrates. Furthermore, each curve in the optimized curve set may includemultiple continuous or discrete value set-point curves for a 24-hourperiod as well as a higher resolution state-of-charge curves that can beused by the system controller 210 to control the distributed energysystem 103. The current curve set uses operational feedback from theenergy source 224 as well as real-time external data (such as localweather conditions) to continually optimize and refine the operation ofthe distributed energy storage system 103. The current curve set can begenerated by components in the operation center 109, and/or, in somecases, locally by the system controller 210.

If communication is lost to optimization engine 1031, the distributedenergy storage system 103 uses the optimized curve set for as long asfeasible, and, if an unpredicted event occurs, can revert back to a setof default parameters set by the default curve. If demand at thedistributed energy storage system 103 begins to push the state-of-chargebeyond the capability of energy source 224 to maintain the currentset-point, the distributed energy storage system 103 adjusts theset-point until the desired state-of-charge can be maintained based onpre-computed back-off values. In some embodiments, distributed energystorage system 103 is configured to use the optimized curve set when ashort-term communication loss occurs (e.g., hours or days) and thedefault curve set when a long-term communication loss occurs (e.g.,several weeks).

Having several sets of curves provides the distributed energy storagesystem 103 with maximum flexibility in the case of abnormal demandconditions and/or loss of communication with the optimization engine1031. If the distributed energy storage system 103 loses communicationand has only one un-optimized demand set-point curve 301 and oneun-optimized battery state-of-charge profile 302 available, thedistributed energy storage system 103 can potentially run out of storedenergy and fail to meet the scheduled requirements. This is becauseun-optimized operational data does not benefit from the ability of theoptimization engine 1031 to “learn” and forecast how a particularelectric load location 104 uses energy. However, in embodiments in whichmultiple demand set-point curves 301 (e.g., battery curves) and/orbattery state-of-charge profile 302 are available to the distributedenergy storage system 103, the current data set is the best optimizeddata set and is typically used instead of the default or optimized datasets. Moreover, having multiple demand set-point curves 301 and/orbattery state-of-charge profiles 302 available in this wayadvantageously includes inherent error checking. Specifically, if loadconditions fall out of range of the latest set of data, the distributedenergy storage system 103 may request a new set of battery curves anddemand set-point data from the central data center.

FIG. 8A illustrates a graph 800 that shows the performance over abusiness day of a conventional (prior art) energy storage system using asingle-demand set-point. Graph 800 includes a battery charge levelcurve, which illustrates the amount of charge contained in thebatteries, a site load curve, a net load curve, and a single demandset-point having a fixed value of 40 kW. The battery charge level curveindicates percentage charge remaining in the battery array over time ofan energy storage system located at an electrical load location, such asa commercial building. The site load curve indicates a varyingelectrical load over time of the electrical load location, and the netload curve indicates a varying net electrical load over time of theelectrical load location, i.e., the quantity of electrical energyactually delivered to the electrical load location from an electricalgrid. Thus, at any point in time in graph 800, the value indicated bythe net load curve is equal to the corresponding site load value minusany energy provided to the electrical load location by the conventionalenergy storage system.

At the beginning of a business day, for example at 7 a.m., the batteryarray of the energy storage system is fully charged (100%) and, overtime the charge drops as energy is provided to the electrical loadlocation by the battery array. Because the single demand set-point isfixed at 40 kW, the energy storage system provides electrical energy tothe electrical load location at whatever rate that prevents the net loadof the electrical load location from exceeding 40 kW. In this way,additional tariffs associated with receiving energy at a rate of greaterthan 40 kW are avoided. However, because the battery array of the energystorage system has very limited capacity, the battery array is quicklydischarged to essentially 0% charge only a few hours after the site loadexceeds the single demand set-point. In the example illustrated in FIG.8A, the battery array is fully discharged by 10 a.m., which is less than2.5 hours after the site load initially exceeds the single demandset-point of 40 kW. Consequently, any demand spikes that occur afterthat time (10 am) cannot be reduced by the conventional energy storagesystem. Because several such spikes may occur later in the business dayin a commercial building, as shown in FIG. 8A, this is highlyundesirable, since tariffs associated with receiving energy at a rategreater than 80 kW and 100 kW are applied to the electrical loadlocation on the business day represented by FIG. 8A.

FIG. 8B illustrates a graph 850 that shows the performance over abusiness day of an energy storage system configured according to one ormore embodiments of the invention. Graph 850 includes a battery chargelevel curve, a site load curve, a net load curve, and a demand set-pointcurve having various values at different times of the business day. Thebattery charge level curve, the site load curve, and the net load curvehave the same definitions provided with respect to graph 800 in FIG. 8A.In contrast to graph 800, graph 850 includes a demand set-point curvethat is adapted to vary with time over the course of a single day, asshown in FIG. 8B. This demand set-point curve (analogous to theabove-described demand set-point curves 301) may be provided by asoftware-based analytics engines, such as the optimization engine 1031,and therefore can be tailored specifically to the charging anddischarging behavior of the electrical load location 104. The demandset-point curve illustrated in graph 850 is a stepped set-point curvethat includes multiple discrete values. In one configuration, asillustrated in FIG. 8B, the stepped set-point curve includes multipleperiods of time, of variable length, in which the set-point ismaintained at a constant level.

FIG. 8C illustrates a graph 860 that shows the performance over abusiness day of an energy storage system configured according to one ormore embodiments of the invention. Graph 860 includes a battery chargelevel curve, a site load curve, a net load curve, and a continuouslyvarying demand set-point curve that varies continuously with time overthe course of a particular time period, such as a business day. Thebattery charge level curve, the site load curve, and the net load curvehave the same definitions provided with respect to graph 800 in FIG. 8A.This continuously varying demand set-point curve may be provided by asoftware-based analytics engines, such as the optimization engine 1031,and therefore can be tailored specifically to the charging anddischarging behavior of the electrical load location 104.

According to some embodiments, an energy storage system associated withgraph 850 and/or graph 860 is configured to maintain a current demandset-point indicated by the demand set-point curve by monitoring the loadof the electric load location and releasing a suitable quantity ofenergy to the electric load location at different times of the day. Thedemand set-point curve, which provides a demand set-point for eachparticular time of day, varies with time and is selected by theset-point controller 204 and delivered to and used by the systemcontroller 210. Consequently, the control parameters created by theoptimization engine 1031 are used to create the demand set-point curvethat takes into account demand spikes that can occur later in thebusiness day. For example, the demand set-points for times earlier in abusiness day may be selected or adjusted to conserve energy in theenergy storage system 224 to allow demand spikes that occur later in thebusiness day to be negated. Thus, the energy storage system dischargesto keep demand at the electrical load location 104 from exceeding thesevarious adjusted set-points. In this way, charges associated with demandspikes can be avoided, even when such demand spikes occur later in abusiness day.

Furthermore, in some embodiments, the rate at which energy is deliveredto the electric load location by the energy storage system 103 is alsomanaged according to a target battery state-of-charge curve received bythe solution manager 202, such as the above-described batterystate-of-charge curve 302. A target battery state-of-charge curveindicates what state-of-charge at any time of the business day ispredicted to allow the demand set-points of the demand set-point curveto be maintained. In such embodiments, when an actual or measured chargeof the energy storage system falls below a specified value in the targetbattery state-of-charge curve, a new demand set-point curve and/ortarget battery state-of-charge curve may be requested from theoptimization engine 1031. Various embodiments of target batterystate-of-charge curves are described herein in conjunction with FIGS.15A-15C.

Thus, in contrast to conventional energy storage systems that use afixed demand set-point, embodiments of the invention facilitatemaximizing the finite energy storage capacity of an energy storagesystem and reducing the unnecessary usage of such systems during lessbeneficial times of the day. For example, by use of the optimizedsolutions provided by the optimization engine, such as including arelatively high target battery state-of-charge earlier in the day,energy in an energy storage system is reserved for preventing morecostly demand peaks that occur later in the day. One will note, by useof the control techniques described herein, and as illustrated in FIG.8B, the state-of-charge of the energy source 224 can be controlled suchthat the battery charge level curve does not reach a 0% state-of-chargeeven when demand management becomes increasingly challenged due toincreasing load.

As noted above, operating parameters such as optimized runtimeparameters 401, demand set-point curves 301, and battery state-of-chargeprofiles 302, are computed through simulations and other statisticaltechniques. FIG. 9 illustrates a control system 900 that includes anoptimization engine 1031 and is configured to generate operatingparameters for an energy storage system controller 210, according to oneembodiment of the invention.

As shown, control system 900 is coupled to a system controller 210 andincludes an optimization engine 1031, a coefficient engine 1032, datacleansing engines 1033 and 1037, a forecast engine 1034, a simulationengine 1035, a solution engine 1036, a utility tariff engine 1039, auniversal tariff data store 1041, an external data store 1042, a sitetelemetry data store 1043 and a risk management engine 1049.

Optimization engine 1031 may be configured as a cloud-based or localcomputing environment designed to compute forecasted solutions for alldistributed energy storage systems 103 associated with control system900. Generally, a solution may include a series of set point arrays(e.g., demand set-point curves 301) as well as forecasted battery curves(e.g., battery state-of-charge curves 302) to enable each distributedenergy storage system 103 to optimize battery usage based on real-timesite load at each electric load location 104 served. These set pointarrays may contain multiple sets of time-based set-points, toaccommodate daily changes in the utility tariff periods that may occur.For example, one set of set-points may be selected to maximize demandcharge savings for a weekday, while another set of set-points may beselected to maximize demand charge savings for a weekend or holiday. Thevalues of the set-points may vary from one type of load location 104 toanother (e.g., hotels, car wash, house, apartments) and thus may beconfigured and controlled by the optimization engine 1031. Each batterycurve is a set of time-based battery state-of-charge zones. If the localstorage medium state-of-charge is monitored to drop into or below acertain zone in the battery curve, then the set-point is adjusted indesired increments to limit the discharging (or increase the charging)of the storage medium. Examples of the controlling components in theoptimization engine that are used to compute forecasted solutions arediscussed below.

The forecast engine 1034 is responsible for generating forward-lookingforecasted load profiles (e.g., power usage as a function of time) for agiven site. To generate the forecast for each of the distributed energystorage systems 103 associated with the control system 900, the forecastengine 1034 gathers all historical information as well as the latestweather and site-specific attributes. The forecast engine 1034 may beconfigured to generate forecast for a distributed energy storage system103 periodically and/or whenever the distributed energy storage system103 requests a forecast for a specific time, such as a remaining portionof a business day in which the current charge in the distributed energystorage system 103 outside a desired range.

The simulation engine 1035 can be configured as a model of eachparticular distributed energy storage system 103. Given an operatingconfiguration of a distributed energy storage system 103, load data, andspecifics of the utility tariff for the electric load location 104served by the distributed energy storage system 103, the simulationengine 1035 can optimize battery usage based on the economics of thetariff. The output of the simulation engine 1035 may be an optimized setpoint array and corresponding battery curve(s). In some embodiments, thesimulation engine 1035 accommodates varying utility measured powerintervals (UMPI). Thus, in such embodiments, the simulation engine 1035can be configured to change a moving average based on the UMPI whengenerating a simulation and evaluating maximum demand.

The utility tariff engine 1039 may be configured as a cloud-based orlocal computing environment, and is responsible for managing thespecifics of utility tariffs used by the optimization engine 1031 togenerate a set point array. Each utility tariff applicable to a specificdistributed energy storage system 103 may be stored in the utilitytariff engine 1039. The utility tariff engine 1039 then uses thisinformation to determine the periods of the day that require optimizedbattery usage. The utility tariff engine 1039 understands and uses theweekday, weekend, and holidays, as well as the changes in rates withineach tariff time period (e.g., minute, hour, day or week), to help forman optimized control solution for one or more of the distributed energystorage systems. The utility tariff engine 1039 may also monitorutility-specific events, such as peak day pricing or changing tariffrates.

FIG. 10 is a block diagram of a general process sequence 1000 used bythe control system 900 to create and deliver forecast and controlinformation to one or more distributed energy storage systems 103, inaccordance with an embodiment of the present invention. The informationcreated in the processing sequence 1000 may include optimized runtimeparameters 401, demand set-point curves 301, and battery state-of-chargeprofiles 302. This information may be used by the system controller 210disposed in the one or more distributed energy storage systems 103 toboth reduce total energy cost to the customer and to help the make thewider electric grid cleaner and more efficient by reducing overall peakpower demand. Additional steps may be added in between the stepsdepicted in FIG. 10, as needed, to optimally control the one or moredistributed energy storage systems 103 associated with control system900. Similarly, one or more steps described herein may also beeliminated as needed without exceeding the scope of the invention.Portions of the process sequence 1000 are also described in greaterdetail in conjunction with FIGS. 11-16.

The process sequence 1000 begins at step 1002, where the coefficientengine 1032 of the control system 900 is used to determine and analyzecoefficients for use in the control system 900. The coefficientsdetermined in step 1002 are used within the forecast engine 1034 andother parts of the control system 900 to form an optimized controlsolution for each distributed energy storage system 103 associated withthe control system 900. Thus, each of the one or more distributed energystorage systems 103 associated with the control system 900 uses adifferent optimized control solution to control the demand at eachcorresponding electric load location 104. The coefficients generallyinclude weightings applied to current and historical data used by thecontrolling software in the forecast engine 1034 to generate a forecastfor a time period (e.g., a day, week, month or year) as to how variousinternal and external site-specific attributes will affect the powerdemand for each of the distributed energy storage systems 103 associatedwith the control system 900. Generally, these coefficients correspond tohistorical weather data, forecasted weather data, additional weatherdata such as cloud cover and sunrise/sunset times, and historical demanddata collected for the site. These coefficients may also correspond toinformation associated with a business or businesses located at theelectric load location 104, such as operating hours, business hours,scheduled maintenance, special events at the location, special eventstaking place nearby that may affect location (e.g., a parade), and otheruseful data that will help forecast the demand at a particularelectrical load location 104.

In step 1004, the coefficient engine 1032 gathers a list of priorcoefficients used by a distributed energy storage system 103 from anoperation database (e.g., memory location) associated with the controlsystem 900, gathers historical premises load information from a loadtelemetry database associated with the control system 900, gathers otherexternal historical data from the external data store 1042, andconstructs a list of coefficients that will be useful to help controleach of the distributed energy storage systems 103. During theconstruction process, the coefficient engine 1032 compares the receiveddata and combines like data to form an optimum data stream for thegeneration of the coefficient that can be used in the forecast engine1034 for each distributed energy storage system 103. In one example,historical and forecasted weather data are compared and combined so thatthe predicted weather conditions can be factored into how the one ormore distributed energy storage systems 103 associated with the controlsystem 900 may react to changes in load at any instant in time at eachrespective electrical load location 104.

In step 1006, the coefficient engine 1032 validates the coefficientscreated in step 1004 to ensure that these coefficients will improve theway the distributed energy storage systems 103 reacts to simulatedchanges in load at any instant in time at each electrical load location104. The validation process may include running historical forecastsusing historical coefficient data sets and comparing results of theforecast to actual load characteristics received from each electricalload location 104. If the results of the forecast qualitatively shows animprovement in how the system reacts with the newly generatedcoefficients versus the current or previously used coefficients, thecoefficient engine 1032 will transfer the validated coefficients to theforecast engine 1034.

In step 1008, the forecast engine 1034 gathers forecasting data for eachdistributed energy storage system 103 associated with control system900. In some embodiments, this information is obtained from existingthird party data providers, such as www.wunderground.com,www.weather.gov, and the like. In some embodiments, the forecasting dataconstructed in step 1008 is based on stored site characteristics foreach distributed energy storage system 103. In such embodiments, thestored site characteristics may be formatted as a business hourstime-based array.

In step 1010, the data-cleansing engine 1037 fills in any missing datausing previously collected information, extrapolating the data or otheruseful techniques and adjusts any time-based intervals to match thedesired interval of the forecast.

In step 1012, the utility tariff engine 1039 gathers specifics regardingeach of the electric utilities' tariff, or tariffs, applicable for eachelectric load location 104 and constructs a data set for the solutionengine 1036 to optimize operation of each distributed energy storagesystem 103 associated with the control system 900.

In step 1014, the forecast engine 1034 uses the latest coefficients fromthe coefficient engine 1032, coefficient data sets, and historical loaddata to generate a forecast of the load for each distributed energystorage system 103 for the appropriate period of time (e.g., month).

In step 1016, the simulation engine 1035 generates simulations of how aparticular distributed energy storage system 103 operates given theforecasted load generated in step 1014. Outputs may include optimizedset points (e.g., demand set-point curves 301) for each day and thestate-of-charge (e.g., battery state-of-charge curves 302) of eachdistributed energy storage system 103 for the forecasted period.

In step 1018, the simulation engine 1035 checks the output of thecurrent day's simulation to ensure the simulation can maintain theoptimized set point strategy described by the demand set-point curves301 generated in step 1016. If the check fails, then the optimizationsimulation is repeated with increased demand set-points in the period ofthe current day's failure and then validated against the new currentsimulation. This iterative process continues until both the optimizedsimulation and the current simulation can successfully maintain theoptimized set point strategy using the same set points.

In step 1020, the simulation engine 1035 combines the simulation outputsof the default, optimized, and current results and packages them fordelivery to the solution engine 1036.

In step 1022, the solution engine 1036 builds the appropriate solutionbattery curve based on the optimal strategy for each distributed energystorage system 103. Battery curve strategies are assigned to eachdistributed energy storage system 103 based on analysis of the loadpredicted for each distributed energy storage system 103. In someembodiments, the possible battery curve strategies that can be assignedinclude a flat battery curve, a stepped battery curve, a continuouslyvarying battery curve, and a forecasted battery curve (batterystate-of-charge profile 302). In such embodiments, a flat battery curveis generally used on load profiles with high demand volatility, astepped battery curve is used when load profiles have heavy activitywithin predictable periods of a day or certain days of the week, and aforecasted battery curve is used when load profiles have large energyevents for sustained periods of time. In some embodiments, as loadchanges over time at a particular electric load location 104, thecorresponding distributed energy storage system 103 can be configured toadjust to the appropriate above-described battery curve strategy.

In step 1024, the solution engine 1035 combines the information outputfrom the battery curve generation and the optimized set points from thesimulation results and packages the solution (e.g., final solution for atime period) for each distributed energy storage system 103.

In step 1026, the risk management engine 1049 analyzes the output ofeach solution and applies a weighting to the forecasted solution toadjust how aggressive or conservative the solution should be for eachparticular distributed energy storage system 103. In some embodiments,the risk management engine 1049 bases such analysis on historicalresults of how forecasted solutions performed in the past as well asreal-time economic drivers to adjust battery usage or ensure demandreduction within individual or groups of distributed energy storagesystems 103 in the field.

In step 1028, the optimization engine 1031 receives the output of therisk management engine 1049 and solution engine 1035 and combines thedata to construct a desired control solution for delivery to systemcontroller 210 of each of the distributed energy storage systems 103associated with control system 900.

FIG. 11 is a block diagram of a process sequence 1100 used by thecoefficient engine 1032 to create updated coefficients for one or moreof the distributed energy storage systems 103 associated with thecontrol system 900, in accordance with an embodiment of the presentinvention. The process sequence 1100 may be performed periodically(e.g., once per month or billing cycle), or whenever optimization engine1031 generates battery curves and/or demand set-point curves for adistributed energy storage system 103. Additional steps may be added toor eliminated from the process sequence 1100 as needed without exceedingthe scope of the invention.

The process sequence begins at step 1101, in which the coefficientengine 1032 collects current coefficient values for one or moredistributed energy storage systems 103. These coefficient values may bestored locally and/or in cloud-based storage.

In step 1102, the coefficient engine 1032 collects historical load datafor the one or more distributed energy storage systems 103 of interest.This historical load data may be stored in an energy storage systemtelemetry database associated with the control system 900, which may bea storage device local to the coefficient engine 1032 or a cloud-basedstorage device.

In step 1103, the coefficient engine 1032 collects external data, suchas weather forecasting information, schedules of events that may occurthat may impact loading on the distributed energy storage systems 103 ofinterest, etc. This external data may be collected via the Internetand/or by any other technically feasible technique.

In step 1104, the coefficient engine 1032 constructs a data array ofhistorical data, external data, and current coefficient values for theone or more distributed energy storage systems 103 of interest.

In step 1105, the coefficient engine 1032 performs a coefficientoptimization algorithm to determine new coefficient values for the oneor more distributed energy storage system 103 of interest. In someembodiments, the coefficient engine 1032 performs a non-linearmathematical analysis of the information included in the data arrayconstructed in step 1104 to determine the improved new coefficientvalues in step 1105. The optimized coefficients are thus generated byuse of the historical data, external data, and current coefficientvalues that have been received.

In step 1106, using the new coefficient values, the coefficient engine1032 performs one or more simulations of the one or more distributedenergy storage system 103 of interest.

In step 1107, the coefficient engine 1032 validates the new coefficientvalues determined in step 1106 by determining if forecasted behavior ofthe one or more distributed energy storage system 103 of interest ismore accurate using the new coefficient values. If accuracy of aforecast is shown to have increased using the new coefficient values inthe simulation, e.g. actual energy consumption is better predicted, theprocessing sequence 1100 proceeds to step 1109. If forecast accuracy isnot increased using the new coefficient values, the processing sequence1100 proceeds to step 1108.

In step 1108, the coefficient engine 1032 flags the new coefficientvalues as less accurate than the current coefficients, and stores thenew coefficient values for future analysis.

In step 1109, the new coefficients are provided to the one or moredistributed energy storage system 103 of interest. In some embodiments,the new coefficient values are also stored in an operations databaseassociated with the control system 900.

FIG. 12 is a block diagram of a process sequence 1200 used by theforecast engine 1034 to generate forward-looking forecasted loadprofiles for a particular electric load location 104 serviced by adistributed energy storage system 103 associated with the control system900. Additional steps may be added to or eliminated from the processsequence 1200 as needed without exceeding the scope of the invention.

The process sequence begins at step 1201, in which the forecast engine1034 receives a request to perform a forecast. Such a request may occurperiodically for each electric load location 104 served by controlsystem 900 or whenever a specific distributed energy storage system 103may benefit from updated coefficients.

In step 1202, the forecast engine 1034 collects available informationpertaining to the specific electric load location 104 of interest,including coefficients, historical load data, telemetry data of thedistributed energy storage system 103, and the like. In someembodiments, weighting of the importance of different historical data isperformed based on the duration of the forecast and/or how far into thefuture the requested forecast period takes place. For example, for aforecast that extends beyond seven to ten days, historical data may beweighted more heavily than weather forecast data that is further outinto the future, since the accuracy of weather forecasting decreasesrapidly with time.

In step 1203, data collected in step 1202 is cleansed. In other words,the collected data are modified to minimize the effect of gaps in datadue to outages, etc. In some embodiments, time intervals associated withdifferent data sets are normalized to a uniform desired time interval,using interpolation, averaging, and the like. For example, algorithmsincluded in the forecast engine 1034 may be configured for data measuredat ten-minute intervals, but a specific data source may only beavailable in fifteen-minute intervals. In some embodiments, after timeintervals are normalized, actual gaps in data are corrected byinterpolation or other techniques. In some embodiments, the procedure bywhich such data are corrected is a function of the duration of the datagap. For example, if the duration of a data gap is on the order ofminutes or seconds, a moving average may be used to make a suitablecorrection. If the duration of a gap is on the order of hours or day,historical data and/or forecast data generated by the forecast engine1034 may be used.

In step 1204, the forecast engine 1034 generates a load forecast for theone or more electric load locations 104 of interest. The load forecastgenerated by the forecast engine 1034 can be used by the simulationengine 1035 to generate a solution result for a particular distributedenergy storage system 103.

FIG. 13 is a block diagram of a process sequence 1300 used by thesimulation engine 1035 to determine optimal set-points and batterycurves, according to an embodiment of the invention. The simulationengine 1035 determines optimal set-points and battery curves for aparticular electric load location 104 serviced by a distributed energystorage system 103 associated with the control system 900. Thesimulation engine 1035 may be used periodically (e.g., once each month,day, billing period, etc.) to determine an optimized solution for thenext such period. Alternatively, in some embodiments, the simulationengine 1034 may be used as part of an intervention, in which adistributed energy storage system 103 is unable to maintain a specifiedstate-of-charge and requires updated battery curves and/or demandset-point curves for a remaining portion of a day, billing cycle, month,etc. Additional steps may be added to or eliminated from the processsequence 1300 as needed without exceeding the scope of the invention.

The process sequence 1300 begins at step 1301, in which the simulationengine 1035 collects a forecasted load for an electric load location 104of interest from the forecast engine 1034 or a database associated withthe control system 900 storing output from the forecast engine 1034.

In step 1302, the simulation engine 1035 collects the current operatingconditions of the distributed energy storage system 103 associated withthe electric load location 104 of interest. It is noted that accuracy ofthe solution generated by the simulation engine 1035 may depend on howrecently the current operation condition data was collected in step1302. For example, when such data is even 15 to 30 minutes old, accuracyof a solution can be adversely affected. This is because a significantportion of energy available in a distributed energy storage system 103can be discharged in that time. Such data related to the distributedenergy storage system 103 may include current energy capacity, currentstate-of-charge, efficiency rating, total energy capacity,charge/discharge limits, etc.

In some embodiments, the simulation engine 1035 also collects applicabletariff information in step 1302, for example from the universal tariffdata store 1041 in FIG. 9. In such embodiments, additional long-termtariff site-specific inform may be collected to ensure that thedistributed energy storage system 103 can operate autonomously for anindefinite period of time. Such information may include holidayinformation and/or major tariff seasonal changes.

In step 1303, the simulation engine 1035 generates a default solution(using a “default” battery curve and demand set-point curve) for thetime period for which the simulation is requested (e.g., a day, a month,a billing period, a remaining portion of a day, etc.). This simulationillustrates how well the distributed energy storage system 103 performsby providing a “default” battery drain curve for the time period ofinterest.

In step 1304, the simulation engine 1035 identifies the “peak” period ofthe billing period or other time period of interest. This peak period,or peak day, is the day or time period during which the distributedenergy storage system 103 typically has the lowest state-of-charge dueto higher power usage at the electric load location at these times.Thus, the peak day is the most difficult day during a billing period forthe distributed energy storage system 103 to operate within desiredparameters.

In step 1305, the simulation engine 1035 performs an analysisdetermining an optimized demand set-point curve and battery curve forthe peak period or peak day determined in step 1304. Generally, in step1035, tariff information is used to determine an economically optimal orotherwise efficient way in which to provide energy to the electricalload location 104.

In step 1306, the simulation engine 1035 runs a simulation using agenerated optimized solution for the current day, i.e., using the loadtelemetry of the electric load location 104 of interest for the currentday and the optimized battery state-of-charge profile and demandset-point curve determined in step 1305 for the distributed energystorage system 103.

In step 1307, the simulation engine 1035 checks the validity of theoptimized solution by checking whether or not the state-of-charge of thedistributed energy storage system 103 is reduced below a desired minimumor other threshold value during the simulation in step 1306. If thestate-of-charge remains greater than the minimum threshold valuethroughout the simulation, process sequence 1300 proceeds to step 1308.If the state-of-charge drops below the minimum threshold value duringthe simulation, the process sequence 1300 proceeds to step 1309. Thus,in step 1307, proper operation of the new solution is confirmed prior tobeing distributed to the distributed energy storage system 103.

In step 1308, the simulation engine 1035 outputs the solution result tothe solution engine 1036 for generating a solution for the distributedenergy storage system 103. The solution result may include default,optimized, and current simulation results.

In step 1309, the simulation engine 1035 increases a set-point value inthe time period or periods in which the state-of-charge of thedistributed energy storage system 103 falls below the desired minimum orother threshold value. In some embodiments, the set-point value isincreased by a relatively small amount, for example on the order ofabout 1 kW for an industrial location. The process sequence 1300 thenproceeds back to step 1305 and continues until an optimized solution isobtained in which the state of charge of the distributed energy storagesystem 103 is reduced below a desired minimum or other threshold value.

FIG. 14 is a block diagram of a process sequence 1400 used by thesolution engine 1036 to generate a solution of optimal set-points andbattery curves for a distributed energy storage system 103. Additionalsteps may be added to or eliminated from the process sequence 1400 asneeded without exceeding the scope of the invention.

The process sequence 1400 begins at step 1401, in which the solutionengine 1036 collects the current battery curve strategy and batteryparameters (e.g., battery power, maximum charge capacity, etc.) for adistributed energy storage system 103.

In step 1402, the solution engine 1036 determines an appropriate batterycurve strategy for the distributed energy storage system 103.Specifically, the solution engine 1036 selects one of a flat, stepped,continuously varying, or forecasted battery curve, based on previousbehavior (i.e., historical load telemetry) for the distributed energystorage system 103 of interest. Other configurations of battery curvemay also be selected by the solution engine 1036 in step 1402.Therefore, in one example, as the state-of-charge of the energy source224 drops due to an increased load the system controller 210 willprovide power to the electric load location 104 based on the controlstrategy defined by the battery curve.

FIG. 15A illustrates one embodiment of a flat battery curve 1510. Asshown, flat battery curve 1510 includes multiple target state-of-chargecurves 1511. Each of the target state-of-charge curves 1511 indicates atarget minimum state-of-charge for a distributed energy storage system103 over a specific time period, for example for a single 24-hourperiod. In other words, each target state-of-charge curve 1511 providesa threshold state-of-charge value at any point in time. Whenstate-of-charge is below this threshold value, the distributed energystorage system 103 is considered to be below an expected state-of-chargeand helps to define how strongly the distributed energy storage system103 should currently react to the changing state-of-charge so that thedistributed energy storage system 103 can effectively respond to thecurrent demand while also retaining enough energy to eliminate demandspikes at critical times later in the day. Consequently, in response toa current state-of-charge of the distributed energy storage system 103falling below a target state-of-charge curve 1511, the system controller210 adjusts demand set-point (e.g., a demand set-point curve 301), sothat the distributed energy storage system 103 can still meet expectedlater demand.

As the current state-of-charge of the distributed energy storage system103 falls below additional target state-of-charge curves 1511, thesystem controller 210 continues to adjust demand set-points. In someembodiments, such a set-point change is linear. For example, for eachtarget state-of-charge curve 1511 that the current state-of-charge fallsbelow, demand set-points are changed by the same amount, e.g., +1 kW. Inother embodiments, such a set-point change can increase in a non-uniformfashion, such as a proportional response that increases as the currentstate-of-charge falls below additional target state-of-charge curves1511. For example, for the first target state-of-charge curve 1511 thatthe current state-of-charge falls below, demand set-points are changedby +1 kW, for the second +5 kW, for the third +12 kW, and so on.

As shown in FIG. 15A, the target state-of-charge curves 1511 of the flatbattery curve 1510 are substantially flat, and therefore do not varywith time. The flat battery curve 1510 has been shown to be effective oncommercial load site profiles with high volatility, i.e., multipledemand peaks separated by periods of relatively low demand.

FIG. 15B illustrates one embodiment of a stepped battery curve 1520. Asshown, the stepped battery curve 1520 has a variable set-point.Specifically, the stepped batter curve 1520 includes multiplediscontinuous target state-of-charge curves 1521, each indicating atarget minimum state-of-charge for a distributed energy storage system103 over a specific time period. Similar to the flat battery curve 1510,the stepped battery curve 1520 includes multiple curves (e.g., thetarget state-of-charge curves 1521) for any given time. Also, for anyparticular time period, any two target state-of-charge curves 1521 varyby a constant amount with respect to time, since each of the targetstate-of-charge curves 1521 is a substantially horizontal line segment.However, separation between adjacent target state-of-charge curves 1521may be uniform. For example, target state-of-charge curves 1521A and1521B may be separated by the same quantity of charge (e.g., 5% ofbattery charge) as target state-of-charge curves 1521B and 1521C.Alternatively, and as illustrated in FIG. 15B, separation betweenadjacent target state-of-charge curves 1521 may be non-uniform, forexample proportional to distance from the highest target state-of-chargecurve 1521. Unlike the flat battery curve 1510, the value of the targetstate-of-charge curves 1521 vary with time, and can therefore betailored to address multiple heavy demand periods within predictableperiods of a day or certain days of the week. Consequently, each oftarget state-of-charge curves 1521 may be a discontinuous function, asshown in FIG. 15B. In some embodiments, a stepped battery curve 1520 isused to optimize the performance of a distributed energy storage system103. Thus, the general configuration of a stepped battery curve 1520 canbe selected to manage how aggressive a distributed energy storage system103 responds to changes in predicted load. Therefore, stepped batterycurve 1520 can be configured to encourage distributed energy storagesystem 103 to maximize reduction in short-term spikes in demand or tomaximize reduction in overall energy use at an electric load location104. As illustrated in FIG. 15B the system will react more aggressivelyto changing load during the 10:00 to 16:00 time period versus the 0:00to 7:00 and 18:00 to 24:00 time periods. Therefore, the distributedenergy storage system will appropriately respond to demand in the lesscritical times, so that it can retain enough energy to eliminate demandspikes at the critical and/or costly times of the day (i.e., 10:00 to16:00 at this electric load location).

FIG. 15C illustrates one embodiment of a continuously varying batterycurve 1530. As shown, the continuously varying battery curve 1530includes multiple target state-of-charge curves 1531, each indicating atarget minimum state-of-charge for a distributed energy storage system103 over a specific time period. Unlike stepped battery curve 1520, thetarget state-of-charge curves 1531 are continuous curves, and each ofwhich may have a variable spacing in continuously varying battery curve1530. As illustrated in FIG. 15C the system will react more aggressivelyto changing load during the 10:00 to 16:00 time period versus the 0:00to 5:00 and 22:00 to 24:00 time periods. Therefore, the distributedenergy storage system will appropriately respond to the demand in theless critical times, so that it can retain enough energy to eliminatedemand spikes at the critical and/or costly times of the day (i.e.,10:00 to 16:00 at this electric load location).

In some embodiments, flat battery curve 1510, stepped battery curve1520, and/or continuously varying battery curve 1530 may be a forecastedbattery curve. Forecasted battery curve are formed by use of theforecast information received and analyzed by the solution engine, andprovides an improved control in cases where the system can use theforecast information to prevent the system from over-reacting toexpected periods of high use. Forecasted battery curves may be usedeffectively when load profiles have large energy events for sustainedperiods of time. In some embodiments, a forecasted battery curve mayinclude an adjustable recovery period that can be configured to ensurecomplete recovery of the distributed energy storage system 103. Anexample of an adjustable recovery period 1533 is shown in FIG. 15C.

Returning to FIG. 14, in step 1403, after an appropriate battery curvestrategy (flat, stepped, continuously varying, forecasted) for thedistributed energy storage system 103 has been determined by thesolution engine 1036, the solution engine 1036 collects informationregarding the configuration of the distributed energy storage system103.

In step 1404, the solution engine 1036 retrieves a simulation for theperiod of interest, such as an upcoming billing cycle, the remainder ofthe current business day, etc. As described above in conjunction withFIG. 13, the simulation can be generated by the simulation engine 1035,and the solution result may include default, optimized, and currentsimulation results.

In step 1405, the solution engine 1036 constructs aggregatedstate-of-charge battery curves. Specifically, the default, optimized,and current battery curves from the simulation results are combined. Forexample, in some embodiments, a median value of the default, optimized,and current battery curves is constructed. In some embodiments one ormore of the default, optimized, and current battery curves are weighteddifferently. In some embodiments, the aggregated state-of-charge batterycurve can be constructed using information from the default, optimized,and current battery curves (e.g., maximum and minimum values, etc.) inother ways as well. For example, in one embodiment, the default,optimized, and current battery curves are used to generate a maximum,minimum and median value at each time interval, and then a maximum, aminimum, and a median battery curve are constructed.

In step 1406, the solution engine 1036 constructs a battery curve usingthe selected strategy. For example, to construct a flat battery curve,the solution engine 1036 may use maximum and minimum state-of-chargevalues determined in step 1405 and then select intervening targetstate-of-charge curves, similar to target state-of-charge curves 1511 inFIG. 15A, using a resolution parameter that defines separation betweeneach target state-of-charge curve. To construct a stepped battery curve,the solution engine 1036 may use the minimum state-of-charge valuesdetermined in step 1405 to calculate a maximum state-of-charge batterycurve substantially similar to one of target state-of-charge curves 1521in FIG. 15B. To construct a forecasted battery curve, the solutionengine 1036 may construct a master battery curve substantially similarto one of target state-of-charge curves 1531 in FIG. 15C. The solutionengine 1036 may use a combination of the default, optimized, and currentbattery curves included in the solution result from the simulationengine 1035.

In step 1407, the solution engine 1036 outputs a set-point array (e.g.,demand set-point curves 301) and a flat, step, continuously varying, orforecasted battery curve (e.g., battery state-of-charge curves 302).

FIG. 16 is a block diagram of a process sequence 1600 used by theoptimization engine 1031 to generate and distribute an optimal batterycurve for a distributed energy storage system 103. The optimizationengine 1031 may perform process sequence 1600 periodically (e.g., onceeach month, day, billing period, etc.) to determine an optimizedsolution for the next such period. Alternatively, in some embodiments,the optimization engine 1031 may be used as part of an intervention, inwhich a distributed energy storage system 103 is unable to maintain aspecified state-of-charge and requires updated battery curves and/ordemand set-point curves for a remaining portion of a day, billing cycle,month, etc. In addition, in some embodiments, the optimization engine1031 may be used to perform a reconciliation, in which the optimizationengine 1031 audits the performance of a distributed energy storagesystem 103 using historical data. Additional steps may be added to oreliminated from the process sequence 1600 as needed without exceedingthe scope of the invention.

The process sequence 1600 begins at step 1601, in which the optimizationengine 1031 determines whether a scheduled job, an intervention, or areconciliation is requested. If a scheduled job is being performed, theprocess sequence proceeds to step 1611, if an intervention is beingperformed, the process sequence proceeds to step 1621, and if areconciliation is being performed, the process sequence proceeds to step1631.

In step 1611, the optimization engine 1031 receives a solution resultfrom the solution engine 1036 and sends the solution result to theappropriate distributed energy storage system 103. The solution resultmay be a job that is performed on a monthly schedule, a daily schedule,based on a billing cycle, etc.

In step 1621, in which a software intervention is being performed, theoptimization engine 1031 confirms that an intervention should beperformed for the distributed energy storage system 103. For example, ifan intervention has already been performed very recently for the samedistributed energy storage system 103, the optimization engine 1031 maywait for a specified delay time before performing another interventionto allow the previous intervention to take effect.

In step 1622, the optimization engine 1031 generates a forecastedsolution for the remainder of a current time period (e.g., for theremainder of a business day). In some embodiments, more up-to-dateexternal data, such as weather data, may be incorporated into theforecasted solution.

In step 1623, the optimization engine 1031 sends the forecastedsolution, which may include a set-point array (e.g., demand set-pointcurves 301) and/or a flat, stepped, continuously varying, or forecastedbattery curve (e.g., battery state-of-charge curves 302) to thedistributed energy storage system 103.

In step 1631, in which a reconciliation is being performed, theoptimization engine 1031 collects historical load data for thedistributed energy storage system 103 for the current billing cycle, forexample from the site telemetry data store 1043.

In step 1632, the optimization engine 1031 calculates maximum demand foreach period of the utility tariff.

In step 1633, the optimization engine 1031 updates the distributedenergy storage system 103 with the maximum demand values determined instep 1632.

It is noted that the same concept of preparing a matrix of temporallysynchronized charge state and demand set-point data in order to manage adistributed energy storage system can be applied to other embodiments.For example, a grid operator may use such data to control otherdistributed resources, such as a hydroelectric facility trying to matcha certain changing demand while also maintaining a certain water levelin the upstream reservoir. The same concept could be used for amunicipal water district trying to maintain a certain reservoir leveland/or water pressure at an autonomously controlled storage facility.The same concept could also be used to control a demand response systemat a large electric load where the “battery” state is the demandreduction availability.

The process of managing an autonomous storage device can have otherapplications as well. Specifically, any system where a variable demandis preferred to be constant and a distributed supply resource, such asstored energy in the form of compressed air, electricity, or water forexample, is used to create this consistency could benefit from this sortof control system. Furthermore, this process is not restricted by sizeof the supply or demand. For example, a substation configured with anautonomous storage system could automatically balance varying circuitloads by using this process, just as well as a single commercial sitecould use this system to balance its load. As an additional example, ahydroelectric facility (whether pumped storage or not, whether microhydro or larger) could benefit from an autonomous control system tobalance varying demands and circuit loads with the appropriatelyforecasted load data and intelligently generated operating parameters.Municipal water (both waste and drinking) could also benefit from such acontrol method. Water level, water pressure, and electricity generationare all examples of demands that such a control method could help keepconstant with a distributed resource.

This energy storage configuration solution does not necessarily have tobe used only at local sites behind electric utility meters. The samemethods could be used for storage systems distributed across manylocations and electric grids, such as one or more of the electric loadlocations illustrated in FIGS. 1 and 18 (discussed below). These storagesystems would need to communicate back to a central operations center109 via a communication link 109A, which may use one of severalnetworks, such as a wireless cellular network or a wireless wide areaWi-Fi network, cable or fiber optic networks or a wired telephonenetwork.

The discovery and configuration method could be used for storage systemswhose storage medium is something other than stored electricity.Embodiments of the invention, could also be used to control the inputand/or output of thermal storage systems or water storage systems, suchas pumped hydroelectric generation.

Power and Frequency Control

As noted above, the stability of the electrical power grid has beenchanging with an increase in the use of wind and solar generators (e.g.,photovoltaic devices) that are not equipped with governors and displacethe quantity of power generated by traditional synchronous type powergenerators. Not only is wind and solar generation unpredictable, butthese generators also have no automatic synchronous response during theprocess of delivering power to the grid, which creates a significant andgrowing need for automatic frequency balancing grid-connected devices.

Some embodiments of the invention use networked distributed energystorage systems 103 (FIG. 18) located either behind utility meters atone or more electric load sites 104 or installed at interconnectionpoints along the transmission and electric power distribution grid 102to provide a way of automatically balancing the frequency of at leastpart of a local electric grid. Simply, these distributed energy storagesystems 103, which are in communication with each other, discharge(inject power) into the grid 102 during an under frequency event (e.g.,measured frequency is less than desired) and charge (withdraw power)from the grid 102 during an over frequency event (e.g., measuredfrequency is greater than desired).

FIG. 17 is a graph illustrating an example of an electric grid frequencyexcursion event versus time, in which the measured frequency 1701 on theelectric grid 102 starts to droop due to a failure or rapid drop inpower being delivered to the grid 102 from one or more connected powergenerators or other power sources. In one example, the rapid power dropmay be due to a power generator going off-line, clouds blocking lightfrom reaching solar cells at a solar farm or a sudden drop in wind speedat a wind farm. In general, one or more detectors 117 (FIG. 18) willgenerally detect a change in the grid frequency and transmit a signal tothe operations center 109, and/or one of the networked distributedenergy storage systems 103, letting them know of the frequency change.Then one or more of the distributed energy storage systems 103 injectpower into the grid 102 during an under frequency event or withdrawpower from the grid 102 during an over frequency event to correct forthe detected frequency excursion. Referring to FIG. 17, by use of one ormore of the hardware configurations and methods disclosed herein, therapid reaction of the networked distributed energy storage systems 103are able to slow and then return the measured frequency 1701 of thetransmitted power on the electric grid 102 back to a desired level. Inone example, the control system hardware, such as the system controllers210 in each distributed energy storage systems 103, are able to react toinformation received from a sensor 310 (FIG. 3) or the operations center109 regarding a shift in grid frequency on a second or sub-second timescale (e.g., 1 second).

As noted above, each of the distributed energy storage systems 103 thatare networked together may receive a control strategy computed by theoptimization engine 1031 that includes grid services instructions, whichallow the distributed energy storage systems 103 to perform its localtask of controlling the power fluctuations at an electric load location104 and also provide support to the electric grid 102. The grid servicesinstructions may include directed and/or automatic frequency regulation,voltage support, and demand response commands, which typically do notdisrupt the control loop that is optimizing the power provided to localpremise load(s), and are used to allow the distributed energy storagesystem 103 to help support and resolve issues that arise on the greaterelectrical grid, which is outside of the commercial electric loadlocation 104.

FIG. 18 illustrates a plurality of distributed energy storage systems103 that are located either behind utility meters 201 at one or moreelectric load sites 104 (not shown in FIG. 18) and/or installed atinterconnection points along the transmission and electric powerdistribution grid 102, according to one embodiment of the invention. Thedistributed energy storage systems 103 may be in communication withother distributed energy storage systems 103 distributed along theelectric grid 102 and may be in communication with an operations center109. One or more sensors 310 and devices 331 are positioned andconfigured to measure the amount of power and the frequency of the powerdelivered through different regions of the electric grid 102. Thesensors 310 may be in communication with a local distributed energystorage systems 103 via the link 311 (FIG. 3) and/or the operationscenter 109 via the communication link 109C (e.g., wired or wirelesscommunication link).

During operation, in some embodiments, it is desirable that thedistributed energy storage systems 103 not respond to frequencymeasurement signals received from just local sensors, as these couldsimply be the result of local electric grid events and could incorrectlyinitiate a frequency response or correction mode from the distributedenergy storage system 103. The frequency response mode typicallyincludes controlling the charging or discharging of the energy source inthe distributed energy storage system 103 in order to offset undesirablefluctuations in the larger electric grid frequency. Instead, thedistributed energy storage systems 103 generally need to monitor severalpoints in a transmission or distribution segment of the electrical powergrid in order to determine if a wide-scale event has occurred on theelectric grid. In one example, as illustrated in FIG. 18, three sensors310 are positioned along the electric grid 102 to monitor and determinethe fluctuations in the electric grid 102. In this example, the threesensors 310 have measured a grid frequency of 59 Hz, which is less thanthe desired 60 Hz grid frequency. Therefore, in response to the underfrequency event the distributed energy storage systems 103 deliver acontrolled delivery of power 1801 (e.g., current flow) to the grid 102to help correct this problem.

The determination that a wide scale event has occurred may be made via avoting scheme, which is performed by central command, or operationscenter 109 (FIG. 18). For example, if a sudden frequency change isnoticed at several geographically disparate monitoring sites (e.g.,sensors 310), it can be assumed that a power generator or severalimporting transmission lines have failed and the distributed energystorage systems 103 should then enter frequency response mode. Thedistributed energy storage systems 103 could communicate directly withthe frequency monitoring sites (e.g., sensors 310) or the operationscenter 109. The operations center 109 can be configured to monitor allthe frequency monitoring sites and make decision regarding when one ormore (e.g., 3 out of 4) of the distributed energy storage systems 103should enter frequency response mode. Additionally, a grid operatorcould provide a signal to the operations center 109 and/or thedistributed energy storage systems 103 that provides information as towhen and in what direction and magnitude that the frequency response isneeded. This communication would likely happen through a wirelesscellular network or a wired telephone or internet connection, such aslinks 109A discussed above.

In general, the distributed energy storage systems 103 will need torespond in the second or sub-second timeframe in order to respond to theinstantaneous changes in power provided by renewable energy generationsources (e.g., wind, solar). This requires that they have real-time ornear real-time communication with a command source, such as theoperations center 109 and/or provided frequency response signal from anexternal source (e.g., grid operator). All of the local distributedenergy storage systems 103 at an electric load site 104 will also needto be synchronized so that they discharge or charge at nearly the sametime. Typically, a single small distributed energy storage system 103will have little effect on overall grid frequency, but when multiplesmall energy storage systems 103 are synchronized, several megawatts(MW) of grid storage can make an impact on grid frequency. In someembodiments, a distributed energy storage systems 103 are each able toprovide approximately 90 kW-hrs of energy per customer site to theelectric grid. Therefore, 50 distributed energy storage systems 103would be able to provide approximately 1 MW of power correctioninstantaneously over the course of an hour. If the outputs of thedistributed energy storage systems are not synchronized, their frequencyeffects could cancel each other out or cause additional supply anddemand imbalances and further exacerbate the original problem. Thissynchronization would most easily occur if they are connected in nearreal-time with the operations center 109 that is monitoring gridfrequency and/or a grid operator frequency response signal. Thisoperations center 109 would then send down the synchronized frequencyresponse discharge or charge commands to the distributed energy storagesystems 103.

This synchronized response will also have the ability to respond in asimilar fashion as a synchronous generator, having programmablecharacteristics such as droop speed control (e.g., control the rate offrequency droop) and response time. These synchronous generatorcharacteristics reside in a software program that a processor in thecontrol system in the operations center 109 uses to control thefrequency of the grid. The operations center 109 can utilize the systemcontroller 210 in the distributed energy storage devices 103 in such away as to deliver power such that it appears to have come from astandard synchronous generator. Due to the massive amount of rotatingmass in traditional synchronous generators, these generators have asignificant amount of inertia that both helps and harms their ability tocompensate for changing load conditions. In some cases, the synchronizeddistributed energy storage systems 103 can be programmed to havecomplementary power delivery control and speed characteristics relativeto traditional synchronous generators to help coordinate and controlresponse to grid frequency excursion events. In some embodiments, thedistributed energy storage systems 103 are able to, but will notnecessarily, respond in a simple “all on” or “all off” scenario. In oneexample, the runtime controller 206 in multiple distributed energystorage systems is programmed to deliver or absorb power at a desirablerate or profile to help stabilize the power flowing through the largerelectric grid.

As noted above, to perform the local control at the electric loadlocation 104 and provide grid services support, the droop controller 330within the system controller 210 in each of the distributed energystorage systems 103 is configured to receive frequency measurements froma sensor 310 and a device 331 that are coupled to the electric grid 102via a wired or wireless communication link 311. The measured frequencyis compared to the nominal grid frequency for the operating region, forexample in the United States it would be compared to 60.000 Hz. Whencommanded to be in frequency regulation mode from the distributed systemgateway 325 (FIG. 3), if the frequency of the grid is below the nominalfrequency and out of specified dead band tolerance and correspondingtime delay the droop controller 330 will adjust the offset controller208 (FIG. 3) so that commands to the energy source 224 in thedistributed energy storage systems 103 are altered to increase thedischarge rate. Alternatively, when the grid frequency is measured to begreater than 60.000 Hz the commands to the energy source 224 in thedistributed energy storage systems 103 can be altered to store energy ata greater rate than required. The response characteristics, such as thespeed and magnitude of the response, are parameters that are controlledby the distributed system gateway 325. The distributed system gateway325 can be a cloud-based interface and controller that allows forspecific and taylored grid services commands to be sent to thedistributed energy storage system 103 outside of the control ofoptimization engine 1031. Thus, the droop controller 330, much like theoffset controller 208, can be used as a higher-level system override ofthe control provided by the system controller 210 in each of thedistributed energy storage systems 103. In one example, each of thedistributed energy storage systems 103 are able to control and/orminimize the amount of fluctuation in demand at its electric loadlocation 104. An opt-out feature may also be required to avoid anyimpact on an electric load location customer's demand managementrequirement, if such a requirement exists. In some embodiments, thelocal demand management could be overridden to force the distributedenergy storage system to respond immediately to the frequency responsesignal.

FIG. 19 depicts a flow diagram of a method 1900 that may be used by thesystem controller 210 in a distributed energy storage system 103 torespond to a frequency response signal received from the operationscenter 109 or a sensor 310. In general, when on-line the controlcomponents (e.g., system controller 210) in the distributed energystorage systems 103 are always listening to determine if a frequencyresponse signal is received (Step 1902). The frequency response signalmay be received directly from a sensor 310 or from the operations center109. Next at step 1904, the system controller 210 then checks anddetermines if the received signal calls for a charge event or adischarge event, depending measured on the frequency of the detectedsignal. The distributed energy storage systems 103 then checks itscurrent mode 1906, 1912 (charging, discharging, or idle) of operationand responds appropriately, using at least the droop controller 330and/or off-set controller 208, which are discussed above.

In one example of the method 1900, if a distributed energy storagesystem 103 is in charge mode (consuming energy from the grid 102) forthe purposes of managing local demand at an electric load location andit receives a command (step 1902) to enter frequency response-chargemode, the distributed energy storage system 110 would then continue tocharge. Graphically, as shown in FIG. 19, this control process wouldfollow the steps 1902, 1904, 1912 (far left path), 1916 and then back to1902, where this process could then be repeated until the grid event wasresolved.

Similarly, in another example, if the same distributed energy storagesystem 103 was in discharge (supply) mode, it would wait until acharging event was required and then charge if the frequencyresponse-charge mode was still active. This control process would followthe steps 1902, 1904, 1912 (far right path), and then back to 1902. Insome cases, the control system may recursively repeat this cycle untilthe system switches to a charging mode or until the frequency correctionresponse is not required.

In another example, if a distributed energy storage system is indischarge mode (injecting energy into the grid) for the purposes ofmanaging local demand and it receives a command to also enter frequencyresponse-discharge mode, the distributed energy storage system wouldcontinue to discharge. As shown in FIG. 19, this control process wouldfollow the steps 1902, 1904, 1906 (far right path), 1910 and then backto 1902. This process sequence could then be repeated until the gridevent was resolved.

Similarly, if the same distributed energy storage system was in chargemode and it received a command to also enter frequencyresponse-discharge mode, it would wait until a discharging event wasallowed or required and then discharge if the frequencyresponse-discharge mode was still active. This control process wouldfollow the steps 1902, 1904, 1906 (far left path) and then back to 1902.The control system may recursively repeat this cycle until the systemswitches to a discharging mode or until the frequency correctionresponse is not required.

If the distributed energy storage system is in idle mode, it has batterycapacity available, and it receives a command to enter frequencyresponse mode, it would charge or discharge power to the electric linedepending on what frequency response mode is required. In one example,if the distributed energy storage system received a command to enterfrequency response-discharge mode the control process would follow thesteps 1902, 1904, 1906 (middle path), 1910 and then back to 1902. Inanother example, if the distributed energy storage system received acommand to enter frequency response-charge mode the control processwould follow the steps 1902, 1904, 1912 (middle path), 1916 and thenback to 1902.

Finally, if there is generation, such as a wind turbine or solar PVpanels, at the storage site, the distributed energy storage system 103could work with these power generating components in such a way as tominimize the amount of oversupply during an over frequency event orminimize the affect of low power generation and supply in an underfrequency event. For example, if there is an over frequency event andthe local on-site generation is generating power, the local distributedenergy storage system 103 could enter charge mode in order to absorbthis excess generation and help mitigate this over frequency scenario.Additionally, the distributed energy storage system 103 could havedirect control over the local generation's inverter so as to connect anddisconnect it from the grid in order to respond to frequency events.This control could be a simple electrically controlled switch on thegrid input side of the local generation, or the control could occur viasoftware execution through the local generation's controller, or throughuse of the system controller 210 in the distributed energy storagesystem 103. This switching would likely be controlled from the localdistributed energy storage system's control and communications device,but could also be controlled from an external central command center.

This method for mitigating frequency imbalances could be used over anygeographic area, whether within city limits or across interconnectedgrid spanning across the country.

While the foregoing is directed to embodiments of the present invention,other and further embodiments of the invention may be devised withoutdeparting from the basic scope thereof, and the scope thereof isdetermined by the claims that follow.

1. A system for managing power, comprising: a first energy storagedevice that is in electric communication with a first electric powerline that is connected to at least a portion of an electric powerdistribution grid; and a first controller that is in communication withthe first energy storage device and a first sensor, wherein the firstcontroller controls a transfer of energy between the first electricpower line and the first energy storage device based on informationgenerated by the first sensor.
 2. The system of claim 1, furthercomprising: a second energy storage device that is in electriccommunication with a second electric power line that is connected to atleast a portion of the electric power distribution grid; a secondcontroller that is connected to the second energy storage device andcontrols a transfer of energy between the second electric power line andthe second energy storage device; and a grid services controller thattransmits a control signal to the first and second controllers based oninformation generated by the first sensor.
 3. The system of claim 2,further comprising a second sensor that measures power transmittedthrough the second electric power line, and wherein the grid servicescontroller controls a transfer of energy between the first electricpower line and the first energy storage device and the second electricpower line and the second energy storage device based on informationgenerated by the first sensor and the second sensor.
 4. The system ofclaim 1, further comprising an operation center that: receives, from thefirst sensor, information relating to a frequency of the powertransmitted through the first electric power line, receives, from asecond sensor, information relating to a frequency of the powertransmitted through a second electric power line that is connected to atleast a portion of the electric power distribution grid, and transmits,to the first controller, a control signal derived from the informationgenerated by the first sensor.
 5. The system of claim 1, furthercomprising: a second sensor that measures a frequency of powertransmitted through a second electric power line that is connected to atleast a portion of the electric power distribution grid; a second energystorage device that is in electric communication with a second electricpower line that is connected to at least a portion of the electric powerdistribution grid; and a second controller that is connected to thesecond energy storage device and controls a transfer of energy betweenthe second electric power line and the second energy storage devicebased on information generated by the first or the second sensor.
 6. Thesystem of claim 1, wherein the first energy source comprises a battery.7. The system of claim 1, further comprising a first controller and anoptimization engine, wherein the optimization engine receives one ormore external inputs and creates a set of operating parameters based onthe one or more external inputs, and wherein the first controllerreceives the set of operating parameters and controls an amount ofenergy flowing through the first electric power based on the set ofoperating parameters.
 8. The system of claim 7, wherein the set ofoperating parameters is derived from simulations of energy use at anelectric load location in which the first energy storage device isdisposed, and wherein the simulations are performed by the optimizationengine.
 9. The system of claim 7, wherein the one or more externalinputs are selected from a group consisting of weather information,sunrise and sunset information, power usage cost information, utility'sbilling period, geographic location, local solar production, localincident light, customer type, electric load location buildingspecifications, grid operator data, and time data.
 10. The system ofclaim 1, further comprising a first bidirectional power converter thatis connected between the first energy source and the first electricpower line and controls the transfer of energy based on a monitoredfrequency of the power transmitted through the first electric powerline.
 11. The system of claim 1, wherein a power source is connected tothe first electric power line, the power source comprising a windturbine or a photovoltaic device.
 12. The system of claim 1, wherein apower source is connected to the first electric power line, and thefirst sensor is connected to the first electric power line near a pointon the first electric power line where the power source is connected.13. The system of claim 1, wherein the sensor measures a frequency ofthe power transmitted through the first electric power line
 13. A systemfor managing power, comprising: a first energy storage device that is inelectric communication with a first electric power line that isconnected to at least a portion of an electric power distribution grid;a second energy storage device that is in electric communication with asecond electric power line that is connected to at least a portion ofthe electric power distribution grid; and a grid services controllerthat transmits a control signal to the first and second controllersbased on information generated by the first sensor.
 14. The system ofclaim 13, wherein the first controller adjusts a transfer of energybetween the first electric power line and the first energy storagedevice based on the received control signal, and the second controlleradjusts a transfer of energy between the second electric power line andthe second energy storage device based on the received control signal.15. The system of claim 14, further comprising: a second sensor thatmeasures a frequency of power transmitted through the second electricpower line, wherein the control signal also is derived from theinformation generated by the second sensor, and wherein the firstcontroller adjusts a transfer of energy between the first electric powerline and the first energy storage device based on the received controlsignal, and the second controller adjusts a transfer of energy betweenthe second electric power line and the second energy storage devicebased on the received control signal.
 16. The system of claim 13,further comprising an operation center that is configured to: receiveinformation relating to a frequency of the power transmitted through thefirst electric power line from the first sensor, receive informationrelating to a frequency of the power transmitted through the secondelectric power line from a second sensor, and transfer a control signalto the grid services controller that is derived from the informationgenerated by the first sensor and the second sensor.
 17. The system ofclaim 13, wherein a power source is connected to the first electricpower line, and the first sensor is connected to the first electricpower line near a point on the first electric power line where the powersource is connected.
 18. The system of claim 13, wherein the systemfurther comprises: an optimization engine that receives one or moreexternal inputs and creates a set of operating parameters based on theone or more external inputs, wherein the first controller receives thecreated operating parameters and controls an amount of energy flowingthrough the first electric power line based on the operating parameters.19. The system of claim 18, wherein the set of operating parameters arederived from simulations of energy use at an electric load location inwhich the first energy storage device is disposed.
 20. The system ofclaim 18, wherein the one or more external inputs are selected from agroup consisting of weather information, sunrise and sunset information,power usage cost information, utility's billing period, geographiclocation, local solar production, local incident light, customer type,electric load location building specifications, grid operator data andtime data.
 21. A method of managing power at an electric load location,comprising: monitoring a frequency of the power transmitted through anelectric power line that transmits electric power within at least afirst portion of an electric power distribution grid; and controlling atransfer of power between a first energy storage device and the electricpower line based on data received by monitoring the frequency of thepower transmitted through the electric power line.
 22. The method ofclaim 21, further comprising receiving power delivery informationcomprising the frequency of the power transmitted through a secondportion of the electric power distribution grid.
 23. The method of claim21, further comprising controlling the transfer of power between asecond energy storage device and the electric power line based on data